Overview

Dataset statistics

Number of variables73
Number of observations1459
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory832.2 KiB
Average record size in memory584.1 B

Variable types

Numeric28
Categorical44
Boolean1

Alerts

Basement_Height is highly overall correlated with Construction_Year and 5 other fieldsHigh correlation
Bedroom_Above_Grade is highly overall correlated with Grade_Living_Area and 2 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
Building_Class is highly overall correlated with Half_Bathroom_Above_Grade and 2 other fieldsHigh correlation
Construction_Year is highly overall correlated with Basement_Height and 5 other fieldsHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
Exterior_Material is highly overall correlated with Garage_Built_Year and 4 other fieldsHigh correlation
Fireplaces is highly overall correlated with Sale_PriceHigh correlation
First_Floor_Area is highly overall correlated with Sale_Price and 1 other fieldsHigh correlation
Foundation_Type is highly overall correlated with Construction_Year and 2 other fieldsHigh correlation
Full_Bathroom_Above_Grade is highly overall correlated with Grade_Living_Area and 3 other fieldsHigh correlation
Garage_Built_Year is highly overall correlated with Basement_Height and 4 other fieldsHigh correlation
Garage_Condition is highly overall correlated with Garage_QualityHigh correlation
Garage_Finish_Year is highly overall correlated with Overall_Material and 1 other fieldsHigh correlation
Garage_Quality is highly overall correlated with Garage_ConditionHigh correlation
Garage_Size is highly overall correlated with Grade_Living_Area and 2 other fieldsHigh correlation
Grade_Living_Area is highly overall correlated with Bedroom_Above_Grade and 6 other fieldsHigh correlation
Half_Bathroom_Above_Grade is highly overall correlated with Building_Class and 1 other fieldsHigh correlation
Heating_Quality is highly overall correlated with Remodel_YearHigh correlation
House_Design is highly overall correlated with Building_ClassHigh correlation
House_Type is highly overall correlated with Building_ClassHigh correlation
Kitchen_Quality is highly overall correlated with Exterior_Material and 3 other fieldsHigh correlation
Neighborhood is highly overall correlated with Basement_Height and 1 other fieldsHigh correlation
Overall_Material is highly overall correlated with Basement_Height and 9 other fieldsHigh correlation
Remodel_Year is highly overall correlated with Basement_Height and 8 other fieldsHigh correlation
Rooms_Above_Grade is highly overall correlated with Bedroom_Above_Grade and 4 other fieldsHigh correlation
Sale_Price is highly overall correlated with Basement_Height and 13 other fieldsHigh correlation
Second_Floor_Area is highly overall correlated with Bedroom_Above_Grade and 3 other fieldsHigh correlation
Total_Basement_Area is highly overall correlated with First_Floor_Area and 1 other fieldsHigh correlation
Zoning_Class is highly overall correlated with NeighborhoodHigh correlation
Zoning_Class is highly imbalanced (56.9%)Imbalance
Road_Type is highly imbalanced (96.1%)Imbalance
Land_Outline is highly imbalanced (68.3%)Imbalance
Utility_Type is highly imbalanced (99.2%)Imbalance
Property_Slope is highly imbalanced (78.8%)Imbalance
Condition1 is highly imbalanced (71.7%)Imbalance
Condition2 is highly imbalanced (96.4%)Imbalance
House_Type is highly imbalanced (59.4%)Imbalance
Roof_Design is highly imbalanced (65.1%)Imbalance
Roof_Quality is highly imbalanced (94.4%)Imbalance
Exterior_Condition is highly imbalanced (72.8%)Imbalance
Basement_Condition is highly imbalanced (76.3%)Imbalance
BsmtFinType2 is highly imbalanced (70.8%)Imbalance
Heating_Type is highly imbalanced (92.6%)Imbalance
Air_Conditioning is highly imbalanced (65.3%)Imbalance
Electrical_System is highly imbalanced (78.2%)Imbalance
Underground_Half_Bathroom is highly imbalanced (79.7%)Imbalance
Kitchen_Above_Grade is highly imbalanced (85.7%)Imbalance
Functional_Rate is highly imbalanced (83.0%)Imbalance
Garage is highly imbalanced (53.1%)Imbalance
Garage_Quality is highly imbalanced (85.8%)Imbalance
Garage_Condition is highly imbalanced (88.1%)Imbalance
Pavedd_Drive is highly imbalanced (69.9%)Imbalance
Sale_Type is highly imbalanced (75.3%)Imbalance
Sale_Condition is highly imbalanced (62.5%)Imbalance
Miscellaneous_Value is highly skewed (γ1 = 24.46844101)Skewed
Garage_Area has unique valuesUnique
W_Deck_Area has unique valuesUnique
Open_Lobby_Area has unique valuesUnique
Enclosed_Lobby_Area has unique valuesUnique
Brick_Veneer_Area has 860 (58.9%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtFinSF2 has 1293 (88.6%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
Total_Basement_Area has 37 (2.5%) zerosZeros
Second_Floor_Area has 828 (56.8%) zerosZeros
LowQualFinSF has 1433 (98.2%) zerosZeros
Three_Season_Lobby_Area has 1435 (98.4%) zerosZeros
Screen_Lobby_Area has 1343 (92.0%) zerosZeros
Pool_Area has 1452 (99.5%) zerosZeros
Miscellaneous_Value has 1407 (96.4%) zerosZeros

Reproduction

Analysis started2024-07-11 03:18:40.881485
Analysis finished2024-07-11 03:22:51.834127
Duration4 minutes and 10.95 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Building_Class
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.92255
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:22:51.970696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.304033
Coefficient of variation (CV)0.74318583
Kurtosis1.5780661
Mean56.92255
Median Absolute Deviation (MAD)30
Skewness1.4069376
Sum83050
Variance1789.6312
MonotonicityNot monotonic
2024-07-11T03:22:52.187555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 535
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 535
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 299
20.5%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 87
 
6.0%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 299
20.5%

Zoning_Class
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
RLD
1150 
RMD
218 
FVR
 
65
RHD
 
16
Commer
 
10

Length

Max length6
Median length3
Mean length3.020562
Min length3

Characters and Unicode

Total characters4407
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRLD
2nd rowRLD
3rd rowRLD
4th rowRLD
5th rowRLD

Common Values

ValueCountFrequency (%)
RLD 1150
78.8%
RMD 218
 
14.9%
FVR 65
 
4.5%
RHD 16
 
1.1%
Commer 10
 
0.7%

Length

2024-07-11T03:22:52.451600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:52.755580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rld 1150
78.8%
rmd 218
 
14.9%
fvr 65
 
4.5%
rhd 16
 
1.1%
commer 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1449
32.9%
D 1384
31.4%
L 1150
26.1%
M 218
 
4.9%
F 65
 
1.5%
V 65
 
1.5%
m 20
 
0.5%
H 16
 
0.4%
C 10
 
0.2%
o 10
 
0.2%
Other values (2) 20
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1449
32.9%
D 1384
31.4%
L 1150
26.1%
M 218
 
4.9%
F 65
 
1.5%
V 65
 
1.5%
m 20
 
0.5%
H 16
 
0.4%
C 10
 
0.2%
o 10
 
0.2%
Other values (2) 20
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1449
32.9%
D 1384
31.4%
L 1150
26.1%
M 218
 
4.9%
F 65
 
1.5%
V 65
 
1.5%
m 20
 
0.5%
H 16
 
0.4%
C 10
 
0.2%
o 10
 
0.2%
Other values (2) 20
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1449
32.9%
D 1384
31.4%
L 1150
26.1%
M 218
 
4.9%
F 65
 
1.5%
V 65
 
1.5%
m 20
 
0.5%
H 16
 
0.4%
C 10
 
0.2%
o 10
 
0.2%
Other values (2) 20
 
0.5%

Lot_Size
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10517.225
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:22:53.015580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3307.4
Q17549
median9477
Q311603
95-th percentile17402.3
Maximum215245
Range213945
Interquartile range (IQR)4054

Descriptive statistics

Standard deviation9984.6757
Coefficient of variation (CV)0.94936404
Kurtosis203.10149
Mean10517.225
Median Absolute Deviation (MAD)2001
Skewness12.203438
Sum15344632
Variance99693749
MonotonicityNot monotonic
2024-07-11T03:22:53.292217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
8400 14
 
1.0%
10800 14
 
1.0%
9000 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
8125 8
 
0.5%
6240 8
 
0.5%
Other values (1063) 1316
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Road_Type
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Paved
1453 
Gravel
 
6

Length

Max length6
Median length5
Mean length5.0041124
Min length5

Characters and Unicode

Total characters7301
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaved
2nd rowPaved
3rd rowPaved
4th rowPaved
5th rowPaved

Common Values

ValueCountFrequency (%)
Paved 1453
99.6%
Gravel 6
 
0.4%

Length

2024-07-11T03:22:53.554937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:53.816508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
paved 1453
99.6%
gravel 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 1459
20.0%
v 1459
20.0%
e 1459
20.0%
P 1453
19.9%
d 1453
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1459
20.0%
v 1459
20.0%
e 1459
20.0%
P 1453
19.9%
d 1453
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1459
20.0%
v 1459
20.0%
e 1459
20.0%
P 1453
19.9%
d 1453
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1459
20.0%
v 1459
20.0%
e 1459
20.0%
P 1453
19.9%
d 1453
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Property_Shape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Reg
924 
IR1
484 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 924
63.3%
IR1 484
33.2%
IR2 41
 
2.8%
IR3 10
 
0.7%

Length

2024-07-11T03:22:54.018986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:54.280869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
reg 924
63.3%
ir1 484
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1459
33.3%
e 924
21.1%
g 924
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1459
33.3%
e 924
21.1%
g 924
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1459
33.3%
e 924
21.1%
g 924
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1459
33.3%
e 924
21.1%
g 924
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Land_Outline
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Lvl
1310 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1310
89.8%
Bnk 63
 
4.3%
HLS 50
 
3.4%
Low 36
 
2.5%

Length

2024-07-11T03:22:54.516881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:54.792861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1310
89.8%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 1396
31.9%
v 1310
29.9%
l 1310
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1396
31.9%
v 1310
29.9%
l 1310
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1396
31.9%
v 1310
29.9%
l 1310
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1396
31.9%
v 1310
29.9%
l 1310
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Utility_Type
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
AllPub
1458 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8754
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1458
99.9%
NoSeWa 1
 
0.1%

Length

2024-07-11T03:22:55.017280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:55.273157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1458
99.9%
nosewa 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2916
33.3%
A 1458
16.7%
P 1458
16.7%
u 1458
16.7%
b 1458
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2916
33.3%
A 1458
16.7%
P 1458
16.7%
u 1458
16.7%
b 1458
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2916
33.3%
A 1458
16.7%
P 1458
16.7%
u 1458
16.7%
b 1458
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2916
33.3%
A 1458
16.7%
P 1458
16.7%
u 1458
16.7%
b 1458
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
I
1051 
C
263 
CulDSac
 
94
FR2P
 
47
FR3P
 
4

Length

Max length7
Median length1
Mean length1.4914325
Min length1

Characters and Unicode

Total characters2176
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowFR2P
3rd rowI
4th rowC
5th rowFR2P

Common Values

ValueCountFrequency (%)
I 1051
72.0%
C 263
 
18.0%
CulDSac 94
 
6.4%
FR2P 47
 
3.2%
FR3P 4
 
0.3%

Length

2024-07-11T03:22:55.486715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:55.885312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
i 1051
72.0%
c 263
 
18.0%
culdsac 94
 
6.4%
fr2p 47
 
3.2%
fr3p 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
I 1051
48.3%
C 357
 
16.4%
u 94
 
4.3%
l 94
 
4.3%
D 94
 
4.3%
S 94
 
4.3%
a 94
 
4.3%
c 94
 
4.3%
F 51
 
2.3%
R 51
 
2.3%
Other values (3) 102
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1051
48.3%
C 357
 
16.4%
u 94
 
4.3%
l 94
 
4.3%
D 94
 
4.3%
S 94
 
4.3%
a 94
 
4.3%
c 94
 
4.3%
F 51
 
2.3%
R 51
 
2.3%
Other values (3) 102
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1051
48.3%
C 357
 
16.4%
u 94
 
4.3%
l 94
 
4.3%
D 94
 
4.3%
S 94
 
4.3%
a 94
 
4.3%
c 94
 
4.3%
F 51
 
2.3%
R 51
 
2.3%
Other values (3) 102
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1051
48.3%
C 357
 
16.4%
u 94
 
4.3%
l 94
 
4.3%
D 94
 
4.3%
S 94
 
4.3%
a 94
 
4.3%
c 94
 
4.3%
F 51
 
2.3%
R 51
 
2.3%
Other values (3) 102
 
4.7%

Property_Slope
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
GS
1381 
MS
 
65
SS
 
13

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGS
2nd rowGS
3rd rowGS
4th rowGS
5th rowGS

Common Values

ValueCountFrequency (%)
GS 1381
94.7%
MS 65
 
4.5%
SS 13
 
0.9%

Length

2024-07-11T03:22:56.300436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:56.754315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gs 1381
94.7%
ms 65
 
4.5%
ss 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
S 1472
50.4%
G 1381
47.3%
M 65
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1472
50.4%
G 1381
47.3%
M 65
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1472
50.4%
G 1381
47.3%
M 65
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1472
50.4%
G 1381
47.3%
M 65
 
2.2%

Neighborhood
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
99 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.4941741
Min length5

Characters and Unicode

Total characters9475
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes 225
15.4%
CollgCr 150
 
10.3%
OldTown 113
 
7.7%
Edwards 99
 
6.8%
Somerst 86
 
5.9%
Gilbert 79
 
5.4%
NridgHt 77
 
5.3%
Sawyer 74
 
5.1%
NWAmes 73
 
5.0%
SawyerW 59
 
4.0%
Other values (15) 424
29.1%

Length

2024-07-11T03:22:57.152057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 225
15.4%
collgcr 150
 
10.3%
oldtown 113
 
7.7%
edwards 99
 
6.8%
somerst 86
 
5.9%
gilbert 79
 
5.4%
nridght 77
 
5.3%
sawyer 74
 
5.1%
nwames 73
 
5.0%
sawyerw 59
 
4.0%
Other values (15) 424
29.1%

Most occurring characters

ValueCountFrequency (%)
r 930
 
9.8%
e 905
 
9.6%
l 622
 
6.6%
d 504
 
5.3%
s 485
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 413
 
4.4%
C 407
 
4.3%
Other values (28) 3862
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 930
 
9.8%
e 905
 
9.6%
l 622
 
6.6%
d 504
 
5.3%
s 485
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 413
 
4.4%
C 407
 
4.3%
Other values (28) 3862
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 930
 
9.8%
e 905
 
9.6%
l 622
 
6.6%
d 504
 
5.3%
s 485
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 413
 
4.4%
C 407
 
4.3%
Other values (28) 3862
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 930
 
9.8%
e 905
 
9.6%
l 622
 
6.6%
d 504
 
5.3%
s 485
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 413
 
4.4%
C 407
 
4.3%
Other values (28) 3862
40.8%

Condition1
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1259 
Feedr
 
81
Artery
 
48
RRAn
 
26
PosN
 
19
Other values (4)
 
26

Length

Max length6
Median length4
Mean length4.121316
Min length4

Characters and Unicode

Total characters6013
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1259
86.3%
Feedr 81
 
5.6%
Artery 48
 
3.3%
RRAn 26
 
1.8%
PosN 19
 
1.3%
RRAe 11
 
0.8%
PosA 8
 
0.5%
RRNn 5
 
0.3%
RRNe 2
 
0.1%

Length

2024-07-11T03:22:57.614062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:58.132661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
norm 1259
86.3%
feedr 81
 
5.6%
artery 48
 
3.3%
rran 26
 
1.8%
posn 19
 
1.3%
rrae 11
 
0.8%
posa 8
 
0.5%
rrnn 5
 
0.3%
rrne 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1436
23.9%
o 1286
21.4%
N 1285
21.4%
m 1259
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1436
23.9%
o 1286
21.4%
N 1285
21.4%
m 1259
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1436
23.9%
o 1286
21.4%
N 1285
21.4%
m 1259
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1436
23.9%
o 1286
21.4%
N 1285
21.4%
m 1259
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Condition2
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1444 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.006854
Min length4

Characters and Unicode

Total characters5846
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1444
99.0%
Feedr 6
 
0.4%
Artery 2
 
0.1%
RRNn 2
 
0.1%
PosN 2
 
0.1%
PosA 1
 
0.1%
RRAn 1
 
0.1%
RRAe 1
 
0.1%

Length

2024-07-11T03:22:58.618237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:22:59.097642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
norm 1444
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%
rrae 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1454
24.9%
N 1448
24.8%
o 1447
24.8%
m 1444
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1454
24.9%
N 1448
24.8%
o 1447
24.8%
m 1444
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1454
24.9%
N 1448
24.8%
o 1447
24.8%
m 1444
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1454
24.9%
N 1448
24.8%
o 1447
24.8%
m 1444
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

House_Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Fam
1219 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.2995202
Min length4

Characters and Unicode

Total characters6273
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 1219
83.6%
TwnhsE 114
 
7.8%
Duplex 52
 
3.6%
Twnhs 43
 
2.9%
2fmCon 31
 
2.1%

Length

2024-07-11T03:22:59.590443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:00.089408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1219
83.6%
twnhse 114
 
7.8%
duplex 52
 
3.6%
twnhs 43
 
2.9%
2fmcon 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 1250
19.9%
1 1219
19.4%
a 1219
19.4%
F 1219
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1250
19.9%
1 1219
19.4%
a 1219
19.4%
F 1219
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1250
19.9%
1 1219
19.4%
a 1219
19.4%
F 1219
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1250
19.9%
1 1219
19.4%
a 1219
19.4%
F 1219
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
7.0%

House_Design
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
725 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9108979
Min length4

Characters and Unicode

Total characters8624
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 725
49.7%
2Story 445
30.5%
1.5Fin 154
 
10.6%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.5%

Length

2024-07-11T03:23:00.355004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:00.663305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1story 725
49.7%
2story 445
30.5%
1.5fin 154
 
10.6%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 1272
14.7%
o 1207
14.0%
r 1207
14.0%
y 1207
14.0%
t 1170
13.6%
1 893
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1272
14.7%
o 1207
14.0%
r 1207
14.0%
y 1207
14.0%
t 1170
13.6%
1 893
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1272
14.7%
o 1207
14.0%
r 1207
14.0%
y 1207
14.0%
t 1170
13.6%
1 893
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1272
14.7%
o 1207
14.0%
r 1207
14.0%
y 1207
14.0%
t 1170
13.6%
1 893
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Overall_Material
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1000685
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:00.919166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3831709
Coefficient of variation (CV)0.22674679
Kurtosis0.096111778
Mean6.1000685
Median Absolute Deviation (MAD)1
Skewness0.21571916
Sum8900
Variance1.9131619
MonotonicityNot monotonic
2024-07-11T03:23:01.141318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 396
27.1%
6 374
25.6%
7 319
21.9%
8 168
11.5%
4 116
 
8.0%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
8.0%
5 396
27.1%
6 374
25.6%
7 319
21.9%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.9%
6 374
25.6%
5 396
27.1%
4 116
 
8.0%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

House_Condition
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5750514
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:01.343853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1131253
Coefficient of variation (CV)0.19966189
Kurtosis1.1051357
Mean5.5750514
Median Absolute Deviation (MAD)0
Skewness0.69368177
Sum8134
Variance1.2390479
MonotonicityNot monotonic
2024-07-11T03:23:01.549400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.3%
6 251
 
17.2%
7 205
 
14.1%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.3%
6 251
 
17.2%
7 205
 
14.1%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.1%
6 251
 
17.2%
5 821
56.3%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

Construction_Year
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2721
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:01.816193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.212814
Coefficient of variation (CV)0.015326557
Kurtosis-0.44080798
Mean1971.2721
Median Absolute Deviation (MAD)25
Skewness-0.61369968
Sum2876086
Variance912.81411
MonotonicityNot monotonic
2024-07-11T03:23:02.136383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1034
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

Remodel_Year
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8794
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:03.615034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645927
Coefficient of variation (CV)0.010401603
Kurtosis-1.2704866
Mean1984.8794
Median Absolute Deviation (MAD)13
Skewness-0.50523913
Sum2895939
Variance426.2543
MonotonicityNot monotonic
2024-07-11T03:23:03.896510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 743
50.9%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

Roof_Design
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gable
1140 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.6223441
Min length3

Characters and Unicode

Total characters6744
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1140
78.1%
Hip 286
 
19.6%
Flat 13
 
0.9%
Gambrel 11
 
0.8%
Mansard 7
 
0.5%
Shed 2
 
0.1%

Length

2024-07-11T03:23:04.207045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:04.499364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gable 1140
78.1%
hip 286
 
19.6%
flat 13
 
0.9%
gambrel 11
 
0.8%
mansard 7
 
0.5%
shed 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1178
17.5%
l 1164
17.3%
e 1153
17.1%
G 1151
17.1%
b 1151
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1178
17.5%
l 1164
17.3%
e 1153
17.1%
G 1151
17.1%
b 1151
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1178
17.5%
l 1164
17.3%
e 1153
17.1%
G 1151
17.1%
b 1151
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1178
17.5%
l 1164
17.3%
e 1153
17.1%
G 1151
17.1%
b 1151
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Roof_Quality
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
SS
1433 
TG
 
11
WSh
 
6
WS
 
5
ME
 
1
Other values (3)
 
3

Length

Max length3
Median length2
Mean length2.0027416
Min length1

Characters and Unicode

Total characters2922
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowSS
2nd rowSS
3rd rowSS
4th rowSS
5th rowSS

Common Values

ValueCountFrequency (%)
SS 1433
98.2%
TG 11
 
0.8%
WSh 6
 
0.4%
WS 5
 
0.3%
ME 1
 
0.1%
M 1
 
0.1%
R 1
 
0.1%
CT 1
 
0.1%

Length

2024-07-11T03:23:04.762196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:05.080714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ss 1433
98.2%
tg 11
 
0.8%
wsh 6
 
0.4%
ws 5
 
0.3%
me 1
 
0.1%
m 1
 
0.1%
r 1
 
0.1%
ct 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 2877
98.5%
T 12
 
0.4%
G 11
 
0.4%
W 11
 
0.4%
h 6
 
0.2%
M 2
 
0.1%
E 1
 
< 0.1%
R 1
 
< 0.1%
C 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2877
98.5%
T 12
 
0.4%
G 11
 
0.4%
W 11
 
0.4%
h 6
 
0.2%
M 2
 
0.1%
E 1
 
< 0.1%
R 1
 
< 0.1%
C 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2877
98.5%
T 12
 
0.4%
G 11
 
0.4%
W 11
 
0.4%
h 6
 
0.2%
M 2
 
0.1%
E 1
 
< 0.1%
R 1
 
< 0.1%
C 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2877
98.5%
T 12
 
0.4%
G 11
 
0.4%
W 11
 
0.4%
h 6
 
0.2%
M 2
 
0.1%
E 1
 
< 0.1%
R 1
 
< 0.1%
C 1
 
< 0.1%

Exterior1st
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
515 
HdBoard
221 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.979438
Min length5

Characters and Unicode

Total characters10183
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 515
35.3%
HdBoard 221
15.1%
MetalSd 220
15.1%
Wd Sdng 206
 
14.1%
Plywood 108
 
7.4%
CemntBd 61
 
4.2%
BrkFace 50
 
3.4%
WdShing 26
 
1.8%
Stucco 25
 
1.7%
AsbShng 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2024-07-11T03:23:05.391549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 515
30.9%
hdboard 221
13.3%
metalsd 220
13.2%
wd 206
 
12.4%
sdng 206
 
12.4%
plywood 108
 
6.5%
cemntbd 61
 
3.7%
brkface 50
 
3.0%
wdshing 26
 
1.6%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1784
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 491
 
4.8%
o 467
 
4.6%
B 335
 
3.3%
Other values (22) 2736
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10183
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1784
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 491
 
4.8%
o 467
 
4.6%
B 335
 
3.3%
Other values (22) 2736
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10183
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1784
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 491
 
4.8%
o 467
 
4.6%
B 335
 
3.3%
Other values (22) 2736
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10183
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1784
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 491
 
4.8%
o 467
 
4.6%
B 335
 
3.3%
Other values (22) 2736
26.9%

Exterior2nd
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
504 
MetalSd
214 
HdBoard
206 
Wd Sdng
197 
Plywood
142 
Other values (11)
196 

Length

Max length7
Median length7
Mean length6.9732694
Min length5

Characters and Unicode

Total characters10174
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 504
34.5%
MetalSd 214
14.7%
HdBoard 206
14.1%
Wd Sdng 197
 
13.5%
Plywood 142
 
9.7%
CmentBd 60
 
4.1%
Wd Shng 38
 
2.6%
Stucco 26
 
1.8%
BrkFace 25
 
1.7%
AsbShng 20
 
1.4%
Other values (6) 27
 
1.9%

Length

2024-07-11T03:23:05.671432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 504
29.6%
wd 235
13.8%
metalsd 214
12.6%
hdboard 206
12.1%
sdng 197
 
11.6%
plywood 142
 
8.3%
cmentbd 60
 
3.5%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
d 1764
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 522
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 445
 
4.4%
t 316
 
3.1%
Other values (23) 2761
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1764
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 522
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 445
 
4.4%
t 316
 
3.1%
Other values (23) 2761
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1764
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 522
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 445
 
4.4%
t 316
 
3.1%
Other values (23) 2761
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1764
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 522
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 445
 
4.4%
t 316
 
3.1%
Other values (23) 2761
27.1%

Brick_Veneer_Area
Real number (ℝ)

ZEROS 

Distinct328
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.75672
Minimum0
Maximum1600
Zeros860
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:05.956747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164.5
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)164.5

Descriptive statistics

Standard deviation180.6106
Coefficient of variation (CV)1.7407123
Kurtosis10.146958
Mean103.75672
Median Absolute Deviation (MAD)0
Skewness2.6753462
Sum151381.05
Variance32620.189
MonotonicityNot monotonic
2024-07-11T03:23:06.290326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 860
58.9%
103.7567195 8
 
0.5%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
Other values (318) 535
36.7%
ValueCountFrequency (%)
0 860
58.9%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

Exterior_Material
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
906 
Gd
487 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 906
62.1%
Gd 487
33.4%
Ex 52
 
3.6%
Fa 14
 
1.0%

Length

2024-07-11T03:23:06.561210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:06.835726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 906
62.1%
gd 487
33.4%
ex 52
 
3.6%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 487
16.7%
d 487
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 487
16.7%
d 487
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 487
16.7%
d 487
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 487
16.7%
d 487
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Exterior_Condition
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1281 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1281
87.8%
Gd 146
 
10.0%
Fa 28
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2024-07-11T03:23:07.071285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:07.350503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1281
87.8%
gd 146
 
10.0%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1281
43.9%
A 1281
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1281
43.9%
A 1281
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1281
43.9%
A 1281
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1281
43.9%
A 1281
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation_Type
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PC
647 
CB
633 
BT
146 
SL
 
24
S
 
6

Length

Max length2
Median length2
Mean length1.9938314
Min length1

Characters and Unicode

Total characters2909
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPC
2nd rowCB
3rd rowPC
4th rowBT
5th rowPC

Common Values

ValueCountFrequency (%)
PC 647
44.3%
CB 633
43.4%
BT 146
 
10.0%
SL 24
 
1.6%
S 6
 
0.4%
W 3
 
0.2%

Length

2024-07-11T03:23:07.588036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:07.865646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pc 647
44.3%
cb 633
43.4%
bt 146
 
10.0%
sl 24
 
1.6%
s 6
 
0.4%
w 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 1280
44.0%
B 779
26.8%
P 647
22.2%
T 146
 
5.0%
S 30
 
1.0%
L 24
 
0.8%
W 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1280
44.0%
B 779
26.8%
P 647
22.2%
T 146
 
5.0%
S 30
 
1.0%
L 24
 
0.8%
W 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1280
44.0%
B 779
26.8%
P 647
22.2%
T 146
 
5.0%
S 30
 
1.0%
L 24
 
0.8%
W 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1280
44.0%
B 779
26.8%
P 647
22.2%
T 146
 
5.0%
S 30
 
1.0%
L 24
 
0.8%
W 3
 
0.1%

Basement_Height
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
685 
Gd
618 
Ex
121 
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 685
46.9%
Gd 618
42.4%
Ex 121
 
8.3%
Fa 35
 
2.4%

Length

2024-07-11T03:23:08.110057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:08.392488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 685
46.9%
gd 618
42.4%
ex 121
 
8.3%
fa 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
T 685
23.5%
A 685
23.5%
G 618
21.2%
d 618
21.2%
E 121
 
4.1%
x 121
 
4.1%
F 35
 
1.2%
a 35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 685
23.5%
A 685
23.5%
G 618
21.2%
d 618
21.2%
E 121
 
4.1%
x 121
 
4.1%
F 35
 
1.2%
a 35
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 685
23.5%
A 685
23.5%
G 618
21.2%
d 618
21.2%
E 121
 
4.1%
x 121
 
4.1%
F 35
 
1.2%
a 35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 685
23.5%
A 685
23.5%
G 618
21.2%
d 618
21.2%
E 121
 
4.1%
x 121
 
4.1%
F 35
 
1.2%
a 35
 
1.2%

Basement_Condition
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1347 
Gd
 
65
Fa
 
45
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1347
92.3%
Gd 65
 
4.5%
Fa 45
 
3.1%
Po 2
 
0.1%

Length

2024-07-11T03:23:08.621495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:08.875625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1347
92.3%
gd 65
 
4.5%
fa 45
 
3.1%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1347
46.2%
A 1347
46.2%
G 65
 
2.2%
d 65
 
2.2%
F 45
 
1.5%
a 45
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1347
46.2%
A 1347
46.2%
G 65
 
2.2%
d 65
 
2.2%
F 45
 
1.5%
a 45
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1347
46.2%
A 1347
46.2%
G 65
 
2.2%
d 65
 
2.2%
F 45
 
1.5%
a 45
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1347
46.2%
A 1347
46.2%
G 65
 
2.2%
d 65
 
2.2%
F 45
 
1.5%
a 45
 
1.5%
P 2
 
0.1%
o 2
 
0.1%

Exposure_Level
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
No
990 
Av
221 
Gd
134 
Mn
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 990
67.9%
Av 221
 
15.1%
Gd 134
 
9.2%
Mn 114
 
7.8%

Length

2024-07-11T03:23:09.107624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:09.387416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 990
67.9%
av 221
 
15.1%
gd 134
 
9.2%
mn 114
 
7.8%

Most occurring characters

ValueCountFrequency (%)
N 990
33.9%
o 990
33.9%
A 221
 
7.6%
v 221
 
7.6%
G 134
 
4.6%
d 134
 
4.6%
M 114
 
3.9%
n 114
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 990
33.9%
o 990
33.9%
A 221
 
7.6%
v 221
 
7.6%
G 134
 
4.6%
d 134
 
4.6%
M 114
 
3.9%
n 114
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 990
33.9%
o 990
33.9%
A 221
 
7.6%
v 221
 
7.6%
G 134
 
4.6%
d 134
 
4.6%
M 114
 
3.9%
n 114
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 990
33.9%
o 990
33.9%
A 221
 
7.6%
v 221
 
7.6%
G 134
 
4.6%
d 134
 
4.6%
M 114
 
3.9%
n 114
 
3.9%

BsmtFinType1
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Unf
467 
GLQ
418 
ALQ
220 
BLQ
147 
Rec
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 467
32.0%
GLQ 418
28.6%
ALQ 220
15.1%
BLQ 147
 
10.1%
Rec 133
 
9.1%
LwQ 74
 
5.1%

Length

2024-07-11T03:23:09.617625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:09.897013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 467
32.0%
glq 418
28.6%
alq 220
15.1%
blq 147
 
10.1%
rec 133
 
9.1%
lwq 74
 
5.1%

Most occurring characters

ValueCountFrequency (%)
L 859
19.6%
Q 859
19.6%
U 467
10.7%
n 467
10.7%
f 467
10.7%
G 418
9.5%
A 220
 
5.0%
B 147
 
3.4%
R 133
 
3.0%
e 133
 
3.0%
Other values (2) 207
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 859
19.6%
Q 859
19.6%
U 467
10.7%
n 467
10.7%
f 467
10.7%
G 418
9.5%
A 220
 
5.0%
B 147
 
3.4%
R 133
 
3.0%
e 133
 
3.0%
Other values (2) 207
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 859
19.6%
Q 859
19.6%
U 467
10.7%
n 467
10.7%
f 467
10.7%
G 418
9.5%
A 220
 
5.0%
B 147
 
3.4%
R 133
 
3.0%
e 133
 
3.0%
Other values (2) 207
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 859
19.6%
Q 859
19.6%
U 467
10.7%
n 467
10.7%
f 467
10.7%
G 418
9.5%
A 220
 
5.0%
B 147
 
3.4%
R 133
 
3.0%
e 133
 
3.0%
Other values (2) 207
 
4.7%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct636
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.37491
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:10.176013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383
Q3712
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712

Descriptive statistics

Standard deviation456.14219
Coefficient of variation (CV)1.0287957
Kurtosis11.126068
Mean443.37491
Median Absolute Deviation (MAD)383
Skewness1.6874983
Sum646884
Variance208065.69
MonotonicityNot monotonic
2024-07-11T03:23:10.567791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (626) 938
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType2
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Unf
1294 
Rec
 
54
LwQ
 
45
BLQ
 
33
ALQ
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1294
88.7%
Rec 54
 
3.7%
LwQ 45
 
3.1%
BLQ 33
 
2.3%
ALQ 19
 
1.3%
GLQ 14
 
1.0%

Length

2024-07-11T03:23:10.985791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:11.451185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 1294
88.7%
rec 54
 
3.7%
lwq 45
 
3.1%
blq 33
 
2.3%
alq 19
 
1.3%
glq 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
U 1294
29.6%
n 1294
29.6%
f 1294
29.6%
L 111
 
2.5%
Q 111
 
2.5%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 45
 
1.0%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1294
29.6%
n 1294
29.6%
f 1294
29.6%
L 111
 
2.5%
Q 111
 
2.5%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 45
 
1.0%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1294
29.6%
n 1294
29.6%
f 1294
29.6%
L 111
 
2.5%
Q 111
 
2.5%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 45
 
1.0%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1294
29.6%
n 1294
29.6%
f 1294
29.6%
L 111
 
2.5%
Q 111
 
2.5%
R 54
 
1.2%
e 54
 
1.2%
c 54
 
1.2%
w 45
 
1.0%
B 33
 
0.8%
Other values (2) 33
 
0.8%

BsmtFinSF2
Real number (ℝ)

ZEROS 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.382454
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:11.882525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.4
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.2485
Coefficient of variation (CV)3.4764978
Kurtosis20.184015
Mean46.382454
Median Absolute Deviation (MAD)0
Skewness4.264538
Sum67672
Variance26001.079
MonotonicityNot monotonic
2024-07-11T03:23:12.379072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1293
88.6%
180 5
 
0.3%
374 3
 
0.2%
469 2
 
0.1%
64 2
 
0.1%
117 2
 
0.1%
468 2
 
0.1%
182 2
 
0.1%
480 2
 
0.1%
551 2
 
0.1%
Other values (134) 144
 
9.9%
ValueCountFrequency (%)
0 1293
88.6%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct779
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.53598
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:12.871987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223.5
median479
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)584.5

Descriptive statistics

Standard deviation441.87406
Coefficient of variation (CV)0.77858333
Kurtosis0.47406884
Mean567.53598
Median Absolute Deviation (MAD)289
Skewness0.91948504
Sum828035
Variance195252.68
MonotonicityNot monotonic
2024-07-11T03:23:13.367798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
300 7
 
0.5%
600 7
 
0.5%
572 7
 
0.5%
280 6
 
0.4%
672 6
 
0.4%
625 6
 
0.4%
440 6
 
0.4%
Other values (769) 1279
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

Total_Basement_Area
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.2934
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:13.874558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile518.6
Q1795.5
median991
Q31298.5
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)503

Descriptive statistics

Standard deviation438.82491
Coefficient of variation (CV)0.41504556
Kurtosis13.245817
Mean1057.2934
Median Absolute Deviation (MAD)234
Skewness1.5249228
Sum1542591
Variance192567.3
MonotonicityNot monotonic
2024-07-11T03:23:14.400881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
728 12
 
0.8%
768 12
 
0.8%
780 11
 
0.8%
848 11
 
0.8%
Other values (711) 1282
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

Heating_Type
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
GasA
1427 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.0006854
Min length4

Characters and Unicode

Total characters5837
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1427
97.8%
GasW 18
 
1.2%
Grav 7
 
0.5%
Wall 4
 
0.3%
OthW 2
 
0.1%
Floor 1
 
0.1%

Length

2024-07-11T03:23:14.783851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:15.085843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1427
97.8%
gasw 18
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1456
24.9%
G 1452
24.9%
s 1445
24.8%
A 1427
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1456
24.9%
G 1452
24.9%
s 1445
24.8%
A 1427
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1456
24.9%
G 1452
24.9%
s 1445
24.8%
A 1427
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1456
24.9%
G 1452
24.9%
s 1445
24.8%
A 1427
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Heating_Quality
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Ex
741 
TA
428 
Gd
240 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 741
50.8%
TA 428
29.3%
Gd 240
 
16.4%
Fa 49
 
3.4%
Po 1
 
0.1%

Length

2024-07-11T03:23:15.324500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:15.583683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ex 741
50.8%
ta 428
29.3%
gd 240
 
16.4%
fa 49
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 240
 
8.2%
d 240
 
8.2%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 240
 
8.2%
d 240
 
8.2%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 240
 
8.2%
d 240
 
8.2%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 240
 
8.2%
d 240
 
8.2%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Air_Conditioning
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
True
1364 
False
 
95
ValueCountFrequency (%)
True 1364
93.5%
False 95
 
6.5%
2024-07-11T03:23:15.864945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Electrical_System
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
SBrkr
1334 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length5
Median length5
Mean length4.9986292
Min length3

Characters and Unicode

Total characters7293
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1334
91.4%
FuseA 94
 
6.4%
FuseF 27
 
1.9%
FuseP 3
 
0.2%
Mix 1
 
0.1%

Length

2024-07-11T03:23:16.111923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:16.400686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1334
91.4%
fusea 94
 
6.4%
fusef 27
 
1.9%
fusep 3
 
0.2%
mix 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

First_Floor_Area
Real number (ℝ)

HIGH CORRELATION 

Distinct752
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.5627
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:16.658000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.9
Q1882
median1086
Q31391.5
95-th percentile1831.5
Maximum4692
Range4358
Interquartile range (IQR)509.5

Descriptive statistics

Standard deviation386.71255
Coefficient of variation (CV)0.33263801
Kurtosis5.7414746
Mean1162.5627
Median Absolute Deviation (MAD)235
Skewness1.3768561
Sum1696179
Variance149546.6
MonotonicityNot monotonic
2024-07-11T03:23:16.931866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
848 12
 
0.8%
894 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
832 7
 
0.5%
Other values (742) 1337
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

Second_Floor_Area
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347.23029
Minimum0
Maximum2065
Zeros828
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:17.219917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.1
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.58348
Coefficient of variation (CV)1.2573312
Kurtosis-0.55506698
Mean347.23029
Median Absolute Deviation (MAD)0
Skewness0.81198885
Sum506609
Variance190605.13
MonotonicityNot monotonic
2024-07-11T03:23:17.491264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 828
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 828
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8485264
Minimum0
Maximum572
Zeros1433
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:17.744083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.639512
Coefficient of variation (CV)8.3165415
Kurtosis83.174678
Mean5.8485264
Median Absolute Deviation (MAD)0
Skewness9.0081487
Sum8533
Variance2365.8021
MonotonicityNot monotonic
2024-07-11T03:23:17.991688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1433
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1433
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

Grade_Living_Area
Real number (ℝ)

HIGH CORRELATION 

Distinct860
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.6415
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:18.254515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129
median1464
Q31777.5
95-th percentile2466.2
Maximum5642
Range5308
Interquartile range (IQR)648.5

Descriptive statistics

Standard deviation525.61661
Coefficient of variation (CV)0.3467948
Kurtosis4.8904697
Mean1515.6415
Median Absolute Deviation (MAD)326
Skewness1.3655022
Sum2211321
Variance276272.82
MonotonicityNot monotonic
2024-07-11T03:23:18.543480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (850) 1351
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
587 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.7%
1 587
40.2%
2 15
 
1.0%
3 1
 
0.1%

Length

2024-07-11T03:23:18.852089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:19.129117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.7%
1 587
40.2%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.7%
1 587
40.2%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 856
58.7%
1 587
40.2%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 856
58.7%
1 587
40.2%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 856
58.7%
1 587
40.2%
2 15
 
1.0%
3 1
 
0.1%

Underground_Half_Bathroom
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1377 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1377
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2024-07-11T03:23:19.352845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:19.597533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1377
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1377
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1377
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1377
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1377
94.4%
1 80
 
5.5%
2 2
 
0.1%

Full_Bathroom_Above_Grade
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
649 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 649
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2024-07-11T03:23:19.834117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:20.100140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 649
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 649
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 649
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 649
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 649
44.5%
3 33
 
2.3%
0 9
 
0.6%

Half_Bathroom_Above_Grade
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
534 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.6%
1 534
36.6%
2 12
 
0.8%

Length

2024-07-11T03:23:20.320939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:20.567161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.6%
1 534
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.6%
1 534
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 913
62.6%
1 534
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 913
62.6%
1 534
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 913
62.6%
1 534
36.6%
2 12
 
0.8%

Bedroom_Above_Grade
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8663468
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:20.758740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81605025
Coefficient of variation (CV)0.28470046
Kurtosis2.2275829
Mean2.8663468
Median Absolute Deviation (MAD)0
Skewness0.21205753
Sum4182
Variance0.66593802
MonotonicityNot monotonic
2024-07-11T03:23:20.995911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 803
55.0%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 803
55.0%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 803
55.0%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

Kitchen_Above_Grade
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1391 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1391
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2024-07-11T03:23:21.289422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:21.591640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1391
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1391
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1391
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1391
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1391
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Kitchen_Quality
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
734 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 734
50.3%
Gd 586
40.2%
Ex 100
 
6.9%
Fa 39
 
2.7%

Length

2024-07-11T03:23:21.875607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:22.137710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 734
50.3%
gd 586
40.2%
ex 100
 
6.9%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 734
25.2%
A 734
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 734
25.2%
A 734
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 734
25.2%
A 734
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 734
25.2%
A 734
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Rooms_Above_Grade
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5181631
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:22.333956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.625894
Coefficient of variation (CV)0.24944052
Kurtosis0.87804615
Mean6.5181631
Median Absolute Deviation (MAD)1
Skewness0.67554669
Sum9510
Variance2.6435313
MonotonicityNot monotonic
2024-07-11T03:23:22.549947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 401
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 401
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 401
27.5%
5 275
18.8%
4 97
 
6.6%

Functional_Rate
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TF
1359 
MD2
 
34
MD1
 
31
MajD1
 
14
MD
 
14
Other values (3)
 
7

Length

Max length5
Median length2
Mean length2.0836189
Min length2

Characters and Unicode

Total characters3040
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowTF
2nd rowTF
3rd rowTF
4th rowTF
5th rowTF

Common Values

ValueCountFrequency (%)
TF 1359
93.1%
MD2 34
 
2.3%
MD1 31
 
2.1%
MajD1 14
 
1.0%
MD 14
 
1.0%
MajD2 5
 
0.3%
SD 1
 
0.1%
MS 1
 
0.1%

Length

2024-07-11T03:23:22.801763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:23.112461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tf 1359
93.1%
md2 34
 
2.3%
md1 31
 
2.1%
majd1 14
 
1.0%
md 14
 
1.0%
majd2 5
 
0.3%
sd 1
 
0.1%
ms 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1359
44.7%
F 1359
44.7%
M 99
 
3.3%
D 99
 
3.3%
1 45
 
1.5%
2 39
 
1.3%
a 19
 
0.6%
j 19
 
0.6%
S 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1359
44.7%
F 1359
44.7%
M 99
 
3.3%
D 99
 
3.3%
1 45
 
1.5%
2 39
 
1.3%
a 19
 
0.6%
j 19
 
0.6%
S 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1359
44.7%
F 1359
44.7%
M 99
 
3.3%
D 99
 
3.3%
1 45
 
1.5%
2 39
 
1.3%
a 19
 
0.6%
j 19
 
0.6%
S 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1359
44.7%
F 1359
44.7%
M 99
 
3.3%
D 99
 
3.3%
1 45
 
1.5%
2 39
 
1.3%
a 19
 
0.6%
j 19
 
0.6%
S 2
 
0.1%

Fireplaces
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
689 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 689
47.2%
1 650
44.6%
2 115
 
7.9%
3 5
 
0.3%

Length

2024-07-11T03:23:23.367155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:23.624015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 689
47.2%
1 650
44.6%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 689
47.2%
1 650
44.6%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 689
47.2%
1 650
44.6%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 689
47.2%
1 650
44.6%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 689
47.2%
1 650
44.6%
2 115
 
7.9%
3 5
 
0.3%

Garage
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Attchd
950 
Detchd
387 
BuiltIn
 
88
Basment
 
19
CarPort
 
9
Other values (2)
 
6

Length

Max length7
Median length6
Mean length6.0760795
Min length5

Characters and Unicode

Total characters8865
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 950
65.1%
Detchd 387
26.5%
BuiltIn 88
 
6.0%
Basment 19
 
1.3%
CarPort 9
 
0.6%
2TFes 5
 
0.3%
2Types 1
 
0.1%

Length

2024-07-11T03:23:23.870732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:24.201252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
attchd 950
65.1%
detchd 387
26.5%
builtin 88
 
6.0%
basment 19
 
1.3%
carport 9
 
0.6%
2tfes 5
 
0.3%
2types 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 2403
27.1%
c 1337
15.1%
h 1337
15.1%
d 1337
15.1%
A 950
 
10.7%
e 412
 
4.6%
D 387
 
4.4%
n 107
 
1.2%
B 107
 
1.2%
u 88
 
1.0%
Other values (15) 400
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8865
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2403
27.1%
c 1337
15.1%
h 1337
15.1%
d 1337
15.1%
A 950
 
10.7%
e 412
 
4.6%
D 387
 
4.4%
n 107
 
1.2%
B 107
 
1.2%
u 88
 
1.0%
Other values (15) 400
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8865
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2403
27.1%
c 1337
15.1%
h 1337
15.1%
d 1337
15.1%
A 950
 
10.7%
e 412
 
4.6%
D 387
 
4.4%
n 107
 
1.2%
B 107
 
1.2%
u 88
 
1.0%
Other values (15) 400
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8865
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2403
27.1%
c 1337
15.1%
h 1337
15.1%
d 1337
15.1%
A 950
 
10.7%
e 412
 
4.6%
D 387
 
4.4%
n 107
 
1.2%
B 107
 
1.2%
u 88
 
1.0%
Other values (15) 400
 
4.5%

Garage_Built_Year
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.9863
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:24.487907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11962
median1985
Q32003
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.755065
Coefficient of variation (CV)0.012502645
Kurtosis-0.36195312
Mean1979.9863
Median Absolute Deviation (MAD)19
Skewness-0.72041017
Sum2888800
Variance612.81326
MonotonicityNot monotonic
2024-07-11T03:23:24.904087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 146
 
10.0%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
2008 29
 
2.0%
Other values (87) 948
65.0%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
 
0.2%
1914 2
 
0.1%
1915 2
 
0.1%
1916 5
 
0.3%
1918 2
 
0.1%
1920 14
1.0%
1921 3
 
0.2%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
 
2.0%
2007 49
 
3.4%
2006 59
4.0%
2005 146
10.0%
2004 53
 
3.6%
2003 50
 
3.4%
2002 26
 
1.8%
2001 20
 
1.4%

Garage_Finish_Year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Unf
686 
RFn
422 
Fin
351 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4377
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 686
47.0%
RFn 422
28.9%
Fin 351
24.1%

Length

2024-07-11T03:23:25.376911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:25.795072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 686
47.0%
rfn 422
28.9%
fin 351
24.1%

Most occurring characters

ValueCountFrequency (%)
n 1459
33.3%
F 773
17.7%
U 686
15.7%
f 686
15.7%
R 422
 
9.6%
i 351
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1459
33.3%
F 773
17.7%
U 686
15.7%
f 686
15.7%
R 422
 
9.6%
i 351
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1459
33.3%
F 773
17.7%
U 686
15.7%
f 686
15.7%
R 422
 
9.6%
i 351
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1459
33.3%
F 773
17.7%
U 686
15.7%
f 686
15.7%
R 422
 
9.6%
i 351
 
8.0%

Garage_Size
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
368 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.5%
1 368
25.2%
3 181
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Length

2024-07-11T03:23:26.173309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:26.631664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.5%
1 368
25.2%
3 181
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.5%
1 368
25.2%
3 181
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 824
56.5%
1 368
25.2%
3 181
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 824
56.5%
1 368
25.2%
3 181
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 824
56.5%
1 368
25.2%
3 181
 
12.4%
0 81
 
5.6%
4 5
 
0.3%

Garage_Area
Real number (ℝ)

UNIQUE 

Distinct1459
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.93479
Minimum-129.36935
Maximum1147.4881
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)1.3%
Memory size11.5 KiB
2024-07-11T03:23:27.084641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-129.36935
5-th percentile119.1031
Q1329.29049
median471.02812
Q3607.18498
95-th percentile822.15482
Maximum1147.4881
Range1276.8574
Interquartile range (IQR)277.89449

Descriptive statistics

Standard deviation210.66879
Coefficient of variation (CV)0.44734174
Kurtosis-0.079933697
Mean470.93479
Median Absolute Deviation (MAD)139.42754
Skewness0.028702268
Sum687093.85
Variance44381.338
MonotonicityNot monotonic
2024-07-11T03:23:27.584797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1085.793744 1
 
0.1%
828.3568298 1
 
0.1%
168.162022 1
 
0.1%
563.5899113 1
 
0.1%
395.5126781 1
 
0.1%
-11.28700838 1
 
0.1%
126.5635725 1
 
0.1%
320.3321246 1
 
0.1%
424.3400246 1
 
0.1%
667.2084974 1
 
0.1%
Other values (1449) 1449
99.3%
ValueCountFrequency (%)
-129.36935 1
0.1%
-126.4056204 1
0.1%
-124.8322537 1
0.1%
-112.3634615 1
0.1%
-82.2020445 1
0.1%
-81.84389859 1
0.1%
-70.72055468 1
0.1%
-59.87841229 1
0.1%
-58.35289021 1
0.1%
-57.04193714 1
0.1%
ValueCountFrequency (%)
1147.488093 1
0.1%
1085.793744 1
0.1%
1076.811737 1
0.1%
1062.633862 1
0.1%
1054.661106 1
0.1%
1051.583968 1
0.1%
1042.882904 1
0.1%
1028.172837 1
0.1%
1013.645334 1
0.1%
1004.528225 1
0.1%

Garage_Quality
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1391 
Fa
 
48
Gd
 
14
Ex
 
3
Po
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1391
95.3%
Fa 48
 
3.3%
Gd 14
 
1.0%
Ex 3
 
0.2%
Po 3
 
0.2%

Length

2024-07-11T03:23:28.039361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:28.465256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1391
95.3%
fa 48
 
3.3%
gd 14
 
1.0%
ex 3
 
0.2%
po 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 1391
47.7%
A 1391
47.7%
F 48
 
1.6%
a 48
 
1.6%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1391
47.7%
A 1391
47.7%
F 48
 
1.6%
a 48
 
1.6%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1391
47.7%
A 1391
47.7%
F 48
 
1.6%
a 48
 
1.6%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1391
47.7%
A 1391
47.7%
F 48
 
1.6%
a 48
 
1.6%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Garage_Condition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1406 
Fa
 
35
Gd
 
9
Po
 
7
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1406
96.4%
Fa 35
 
2.4%
Gd 9
 
0.6%
Po 7
 
0.5%
Ex 2
 
0.1%

Length

2024-07-11T03:23:28.903420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:29.373333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1406
96.4%
fa 35
 
2.4%
gd 9
 
0.6%
po 7
 
0.5%
ex 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1406
48.2%
A 1406
48.2%
F 35
 
1.2%
a 35
 
1.2%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%
x 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1406
48.2%
A 1406
48.2%
F 35
 
1.2%
a 35
 
1.2%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%
x 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1406
48.2%
A 1406
48.2%
F 35
 
1.2%
a 35
 
1.2%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%
x 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1406
48.2%
A 1406
48.2%
F 35
 
1.2%
a 35
 
1.2%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.2%
o 7
 
0.2%
E 2
 
0.1%
x 2
 
0.1%

Pavedd_Drive
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Y
1339 
N
 
90
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1459
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1339
91.8%
N 90
 
6.2%
P 30
 
2.1%

Length

2024-07-11T03:23:29.611584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:29.864622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y 1339
91.8%
n 90
 
6.2%
p 30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y 1339
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 1339
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 1339
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 1339
91.8%
N 90
 
6.2%
P 30
 
2.1%

W_Deck_Area
Real number (ℝ)

UNIQUE 

Distinct1459
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.015667
Minimum-338.11203
Maximum572.29871
Zeros0
Zeros (%)0.0%
Negative337
Negative (%)23.1%
Memory size11.5 KiB
2024-07-11T03:23:30.099242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-338.11203
5-th percentile-108.27228
Q19.6560262
median92.803628
Q3180.33599
95-th percentile295.54129
Maximum572.29871
Range910.41074
Interquartile range (IQR)170.67996

Descriptive statistics

Standard deviation124.79668
Coefficient of variation (CV)1.3416738
Kurtosis0.060856968
Mean93.015667
Median Absolute Deviation (MAD)85.437165
Skewness-0.046888522
Sum135709.86
Variance15574.212
MonotonicityNot monotonic
2024-07-11T03:23:30.376403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163.7880797 1
 
0.1%
131.8609302 1
 
0.1%
-51.66659744 1
 
0.1%
-25.65432984 1
 
0.1%
-53.8316252 1
 
0.1%
112.1456298 1
 
0.1%
80.35616594 1
 
0.1%
203.8359801 1
 
0.1%
-11.74214909 1
 
0.1%
40.70591959 1
 
0.1%
Other values (1449) 1449
99.3%
ValueCountFrequency (%)
-338.1120307 1
0.1%
-311.0859231 1
0.1%
-307.235997 1
0.1%
-302.516923 1
0.1%
-291.5278827 1
0.1%
-258.3666053 1
0.1%
-240.8640854 1
0.1%
-235.9486047 1
0.1%
-224.5557613 1
0.1%
-206.5605351 1
0.1%
ValueCountFrequency (%)
572.298709 1
0.1%
472.1021466 1
0.1%
429.7341693 1
0.1%
419.919982 1
0.1%
418.9246382 1
0.1%
416.0114149 1
0.1%
414.5076661 1
0.1%
405.3968427 1
0.1%
403.949033 1
0.1%
397.2455121 1
0.1%

Open_Lobby_Area
Real number (ℝ)

UNIQUE 

Distinct1459
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.811902
Minimum-187.14996
Maximum255.36255
Zeros0
Zeros (%)0.0%
Negative357
Negative (%)24.5%
Memory size11.5 KiB
2024-07-11T03:23:30.652512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-187.14996
5-th percentile-61.64739
Q11.8431858
median46.837919
Q396.523486
95-th percentile158.29332
Maximum255.36255
Range442.51251
Interquartile range (IQR)94.6803

Descriptive statistics

Standard deviation67.467586
Coefficient of variation (CV)1.4111044
Kurtosis-0.16720414
Mean47.811902
Median Absolute Deviation (MAD)47.916407
Skewness-0.071551821
Sum69757.564
Variance4551.8752
MonotonicityNot monotonic
2024-07-11T03:23:30.927740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.59611493 1
 
0.1%
74.99625869 1
 
0.1%
-56.25605537 1
 
0.1%
-9.736793101 1
 
0.1%
17.63397485 1
 
0.1%
70.53182623 1
 
0.1%
-34.47834204 1
 
0.1%
66.35584231 1
 
0.1%
60.30751572 1
 
0.1%
11.85816205 1
 
0.1%
Other values (1449) 1449
99.3%
ValueCountFrequency (%)
-187.1499582 1
0.1%
-158.6444955 1
0.1%
-153.4406273 1
0.1%
-152.8348404 1
0.1%
-143.85371 1
0.1%
-143.6791946 1
0.1%
-135.7050552 1
0.1%
-131.6942607 1
0.1%
-127.0539984 1
0.1%
-120.3395999 1
0.1%
ValueCountFrequency (%)
255.3625472 1
0.1%
234.9799811 1
0.1%
234.6755772 1
0.1%
233.5972082 1
0.1%
221.5144804 1
0.1%
220.0211435 1
0.1%
219.2671224 1
0.1%
217.1241162 1
0.1%
207.3851546 1
0.1%
199.5080751 1
0.1%

Enclosed_Lobby_Area
Real number (ℝ)

UNIQUE 

Distinct1459
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.580227
Minimum-164.80739
Maximum225.76271
Zeros0
Zeros (%)0.0%
Negative489
Negative (%)33.5%
Memory size11.5 KiB
2024-07-11T03:23:31.211509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-164.80739
5-th percentile-77.072695
Q1-16.807302
median25.026953
Q366.237591
95-th percentile123.77804
Maximum225.76271
Range390.5701
Interquartile range (IQR)83.044893

Descriptive statistics

Standard deviation61.353507
Coefficient of variation (CV)2.4960513
Kurtosis-0.091927327
Mean24.580227
Median Absolute Deviation (MAD)41.586447
Skewness-0.044954662
Sum35862.551
Variance3764.2528
MonotonicityNot monotonic
2024-07-11T03:23:31.518711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.33793445 1
 
0.1%
51.83597088 1
 
0.1%
3.44179412 1
 
0.1%
137.4484538 1
 
0.1%
-62.70091899 1
 
0.1%
-16.41256596 1
 
0.1%
7.408243523 1
 
0.1%
-73.45357278 1
 
0.1%
57.88094347 1
 
0.1%
-58.44841067 1
 
0.1%
Other values (1449) 1449
99.3%
ValueCountFrequency (%)
-164.8073862 1
0.1%
-162.5656332 1
0.1%
-153.7190358 1
0.1%
-153.1497485 1
0.1%
-143.7239732 1
0.1%
-132.889248 1
0.1%
-131.7195197 1
0.1%
-130.4784315 1
0.1%
-128.6889476 1
0.1%
-127.6716564 1
0.1%
ValueCountFrequency (%)
225.7627141 1
0.1%
197.4338667 1
0.1%
196.0229395 1
0.1%
192.8390493 1
0.1%
190.9902305 1
0.1%
183.7306686 1
0.1%
183.5293171 1
0.1%
182.7689544 1
0.1%
178.232105 1
0.1%
177.3587157 1
0.1%

Three_Season_Lobby_Area
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.411926
Minimum0
Maximum508
Zeros1435
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:31.779752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.327247
Coefficient of variation (CV)8.5955109
Kurtosis123.57497
Mean3.411926
Median Absolute Deviation (MAD)0
Skewness10.300725
Sum4978
Variance860.0874
MonotonicityNot monotonic
2024-07-11T03:23:32.026692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1435
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1435
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

Screen_Lobby_Area
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.071282
Minimum0
Maximum480
Zeros1343
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:32.297071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.775138
Coefficient of variation (CV)3.7007561
Kurtosis18.423508
Mean15.071282
Median Absolute Deviation (MAD)0
Skewness4.1205716
Sum21989
Variance3110.866
MonotonicityNot monotonic
2024-07-11T03:23:32.618556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1343
92.0%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1343
92.0%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

Pool_Area
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7607951
Minimum0
Maximum738
Zeros1452
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:32.860210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.191018
Coefficient of variation (CV)14.557769
Kurtosis223.11271
Mean2.7607951
Median Absolute Deviation (MAD)0
Skewness14.823236
Sum4028
Variance1615.3179
MonotonicityNot monotonic
2024-07-11T03:23:33.068552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1452
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1452
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1452
99.5%

Miscellaneous_Value
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.518849
Minimum0
Maximum15500
Zeros1407
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:33.315636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.29183
Coefficient of variation (CV)11.404066
Kurtosis700.52431
Mean43.518849
Median Absolute Deviation (MAD)0
Skewness24.468441
Sum63494
Variance246305.58
MonotonicityNot monotonic
2024-07-11T03:23:33.577267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1407
96.4%
400 11
 
0.8%
500 8
 
0.5%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
15500 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1407
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 8
 
0.5%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
0.1%
1150 1
 
0.1%
800 1
 
0.1%

Month_Sold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3221385
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:33.824176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7045401
Coefficient of variation (CV)0.42778881
Kurtosis-0.40590861
Mean6.3221385
Median Absolute Deviation (MAD)2
Skewness0.21173967
Sum9224
Variance7.314537
MonotonicityNot monotonic
2024-07-11T03:23:34.042776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 252
17.3%
7 234
16.0%
5 204
14.0%
4 141
9.7%
8 122
8.4%
3 106
7.3%
10 89
 
6.1%
11 79
 
5.4%
9 63
 
4.3%
12 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 106
7.3%
4 141
9.7%
5 204
14.0%
6 252
17.3%
7 234
16.0%
8 122
8.4%
9 63
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 59
 
4.0%
11 79
 
5.4%
10 89
 
6.1%
9 63
 
4.3%
8 122
8.4%
7 234
16.0%
6 252
17.3%
5 204
14.0%
4 141
9.7%
3 106
7.3%

Year_Sold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2009
338 
2007
329 
2006
314 
2008
303 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5836
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 303
20.8%
2010 175
12.0%

Length

2024-07-11T03:23:34.276879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:34.572458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 303
20.8%
2010 175
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 303
 
5.2%
1 175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 303
 
5.2%
1 175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 303
 
5.2%
1 175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2918
50.0%
2 1459
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 303
 
5.2%
1 175
 
3.0%

Sale_Type
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
WD
1266 
New
 
122
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.1583276
Min length2

Characters and Unicode

Total characters3149
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1266
86.8%
New 122
 
8.4%
COD 43
 
2.9%
ConLD 9
 
0.6%
ConLI 5
 
0.3%
ConLw 5
 
0.3%
CWD 4
 
0.3%
Oth 3
 
0.2%
Con 2
 
0.1%

Length

2024-07-11T03:23:34.826672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:35.132911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wd 1266
86.8%
new 122
 
8.4%
cod 43
 
2.9%
conld 9
 
0.6%
conli 5
 
0.3%
conlw 5
 
0.3%
cwd 4
 
0.3%
oth 3
 
0.2%
con 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 1322
42.0%
W 1270
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1322
42.0%
W 1270
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1322
42.0%
W 1270
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1322
42.0%
W 1270
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Sale_Condition
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Normal
1197 
Partial
125 
Abnorml
 
101
Family
 
20
Alloca
 
12

Length

Max length7
Median length6
Mean length6.1576422
Min length6

Characters and Unicode

Total characters8984
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1197
82.0%
Partial 125
 
8.6%
Abnorml 101
 
6.9%
Family 20
 
1.4%
Alloca 12
 
0.8%
AdjLand 4
 
0.3%

Length

2024-07-11T03:23:35.407035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-11T03:23:35.702641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 1197
82.0%
partial 125
 
8.6%
abnorml 101
 
6.9%
family 20
 
1.4%
alloca 12
 
0.8%
adjland 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1483
16.5%
l 1467
16.3%
r 1423
15.8%
m 1318
14.7%
o 1310
14.6%
N 1197
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1483
16.5%
l 1467
16.3%
r 1423
15.8%
m 1318
14.7%
o 1310
14.6%
N 1197
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1483
16.5%
l 1467
16.3%
r 1423
15.8%
m 1318
14.7%
o 1310
14.6%
N 1197
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1483
16.5%
l 1467
16.3%
r 1423
15.8%
m 1318
14.7%
o 1310
14.6%
N 1197
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Sale_Price
Real number (ℝ)

HIGH CORRELATION 

Distinct662
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180944.1
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-11T03:23:35.978584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129950
median163000
Q3214000
95-th percentile326200
Maximum755000
Range720100
Interquartile range (IQR)84050

Descriptive statistics

Standard deviation79464.918
Coefficient of variation (CV)0.43916832
Kurtosis6.5298819
Mean180944.1
Median Absolute Deviation (MAD)38000
Skewness1.88176
Sum2.6399745 × 108
Variance6.3146732 × 109
MonotonicityNot monotonic
2024-07-11T03:23:36.277587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
160000 12
 
0.8%
115000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (652) 1322
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

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2024-07-11T03:19:52.190541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:00.351067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:08.535554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:17.135396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:24.563262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:33.157968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:40.553757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:48.791264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:55.724227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:04.545935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-07-11T03:21:20.395502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:27.530435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:36.884023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:44.409474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:51.814187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:59.793961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:07.356713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:16.664255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:23.765117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:32.540603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:39.620446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:48.443443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:11.749052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:20.323423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:27.194491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:36.387449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:43.801416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:52.445454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:00.773908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:08.786290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:17.498623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:24.816895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:33.403972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:40.793495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:49.042108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:55.987064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:04.807996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:11.987661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:20.645299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:27.936945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:37.142623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:44.800247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:52.086459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:00.191876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:07.612500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:16.940164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:24.041717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:32.802580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:39.866876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:48.691491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:12.022711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:20.559124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:27.429645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:36.640524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:44.115726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:52.700361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:01.131090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:09.057938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:17.856250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:25.054294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:33.652575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:41.052418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:49.291425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:56.227807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:05.082991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:12.232491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:20.900035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:28.323133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:37.394353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:45.142053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:52.317912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:00.554129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:07.872099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:17.195361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:24.284001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:33.079284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:40.120199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:48.932095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:12.291568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:20.792612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:27.673335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:36.884230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:44.496116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:52.957747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:01.513397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:09.296502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:18.228781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:25.306645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:33.898919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:41.285457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:49.528427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:56.466474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:05.312372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:12.490576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:21.147187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:28.691031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:37.637775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:45.496761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:52.573325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:00.969512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:08.119334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:17.414148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:24.542923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:33.334471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:40.350974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:49.194953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:12.542628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:21.049015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:28.163804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:37.155495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:44.855082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:53.213668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:01.903591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:09.557117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:18.664697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:25.552522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:34.739671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:41.525605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:49.769429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:56.722815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:05.564510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:12.796943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:21.406650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:29.129315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:37.889773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:45.840312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:52.817665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:01.374708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:08.376971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:17.658126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:24.782856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:33.599962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:40.604637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:49.456870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:12.816476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:21.322623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:28.426712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:37.428772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:45.219376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:53.477013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:02.345820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:09.817094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:18.951686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:25.808471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:35.001958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:41.776084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:50.049880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:56.984252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:05.838690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:13.205384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:21.666136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:29.498598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:38.155239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:46.187987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:53.102363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:01.653455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:08.635467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:17.936112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:25.065352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:33.882686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:40.857981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:49.711121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:13.064479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:21.567224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:28.670127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:37.672611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:45.594465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:19:53.735467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:02.715037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:10.071311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:19.197605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:26.048743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:35.248742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:42.038378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:50.275735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:20:57.221295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:06.102158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:13.560508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:21.937022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:29.862071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:38.391164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:46.531449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:21:53.335625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:01.914018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:08.898957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:18.164380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:25.316467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:34.140607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-11T03:22:41.181987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-11T03:23:36.689714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Air_ConditioningBasement_ConditionBasement_HeightBedroom_Above_GradeBrick_Veneer_AreaBsmtFinSF1BsmtFinSF2BsmtFinType1BsmtFinType2BsmtUnfSFBuilding_ClassCondition1Condition2Construction_YearElectrical_SystemEnclosed_Lobby_AreaExposure_LevelExterior1stExterior2ndExterior_ConditionExterior_MaterialFireplacesFirst_Floor_AreaFoundation_TypeFull_Bathroom_Above_GradeFunctional_RateGarageGarage_AreaGarage_Built_YearGarage_ConditionGarage_Finish_YearGarage_QualityGarage_SizeGrade_Living_AreaHalf_Bathroom_Above_GradeHeating_QualityHeating_TypeHouse_ConditionHouse_DesignHouse_TypeKitchen_Above_GradeKitchen_QualityLand_OutlineLot_ConfigurationLot_SizeLowQualFinSFMiscellaneous_ValueMonth_SoldNeighborhoodOpen_Lobby_AreaOverall_MaterialPavedd_DrivePool_AreaProperty_ShapeProperty_SlopeRemodel_YearRoad_TypeRoof_DesignRoof_QualityRooms_Above_GradeSale_ConditionSale_PriceSale_TypeScreen_Lobby_AreaSecond_Floor_AreaThree_Season_Lobby_AreaTotal_Basement_AreaUnderground_Full_BathroomUnderground_Half_BathroomUtility_TypeW_Deck_AreaYear_SoldZoning_Class
Air_Conditioning1.0000.2360.2240.0300.1760.1870.0580.1980.0290.010-0.1070.0400.0680.3410.421-0.0170.0930.3490.3300.2000.2780.1960.1610.3650.1030.0890.231-0.0140.1490.2380.2670.1860.2830.1040.1300.3790.4610.0990.2330.2890.2440.3430.1280.0630.106-0.049-0.0090.0150.3830.0120.2480.3360.0180.1080.0000.2750.0400.0550.0000.0440.1130.3130.1280.039-0.0200.0340.2160.1060.0160.000-0.0240.0000.297
Basement_Condition0.2361.0000.192-0.0100.0640.061-0.0330.1000.053-0.023-0.0290.0360.0000.0860.421-0.0490.0550.0870.0710.2080.1520.0200.0120.1370.1440.2190.097-0.0310.0250.1880.1080.2350.099-0.0220.0400.0870.060-0.0090.0840.0380.0610.1170.0610.0290.001-0.0430.013-0.0310.118-0.0220.0150.1370.0200.0450.1350.0070.0000.0510.040-0.0180.0640.0320.0850.008-0.0400.0370.0300.0750.0600.000-0.0280.0530.092
Basement_Height0.2240.1921.0000.070-0.276-0.1510.1440.3270.093-0.157-0.1140.1520.110-0.6800.206-0.0050.2010.3210.3160.1080.4640.180-0.2650.4080.3370.1090.246-0.003-0.5720.1490.4170.1660.398-0.3620.1660.2740.0250.2880.2110.1700.0910.4210.0940.085-0.1350.0830.059-0.0110.5330.049-0.6030.180-0.0180.1390.000-0.5260.0000.1640.040-0.2170.245-0.5910.2460.023-0.139-0.023-0.3520.1100.0470.000-0.0210.0000.189
Bedroom_Above_Grade0.030-0.0100.0701.0000.112-0.0840.0090.1030.0000.1580.0690.0600.000-0.0350.081-0.0100.1000.0820.0670.0000.1710.1070.1410.0860.4480.0160.131-0.036-0.0610.0000.1090.0410.1340.5430.2500.0210.061-0.0040.2420.3020.2330.1310.1100.0000.3380.0210.0130.0510.2060.0010.1220.0980.0720.0330.100-0.0540.0000.1400.1010.6680.1050.2350.0570.0340.511-0.0190.0590.2550.0300.0000.0080.0210.165
Brick_Veneer_Area0.1760.064-0.2760.1121.0000.244-0.0610.0960.0000.0750.0240.0000.1030.4040.0000.0320.0980.0000.0260.0000.2400.1540.3520.0750.1790.0260.0700.0160.2440.0000.1980.0000.1990.3230.1370.0350.000-0.1800.0460.0000.0000.1870.0250.0360.177-0.107-0.0510.0180.182-0.0010.4140.0750.0040.0700.0000.2360.0000.1050.1420.2630.0560.4230.0370.0370.0630.0400.3610.0250.0000.170-0.0060.0390.061
BsmtFinSF10.1870.061-0.151-0.0840.2441.0000.0490.2750.049-0.573-0.1070.1070.3120.1900.057-0.0040.2370.1370.1390.0000.2080.2980.3230.1110.1580.0000.1060.0200.0310.0000.2090.0270.1760.0580.0120.0590.000-0.0120.1140.0000.0000.2090.1380.0460.172-0.0790.005-0.0160.2010.0210.1340.1090.0580.2060.0830.0640.0190.0960.449-0.0500.0820.3020.0720.072-0.1910.0470.4100.3970.0280.0000.0370.0000.091
BsmtFinSF20.058-0.0330.1440.009-0.0610.0491.0000.1910.469-0.270-0.0820.0000.040-0.1110.000-0.0180.0650.0710.0620.0000.0430.0800.0670.0690.0350.1090.0350.013-0.1610.0000.0230.0000.000-0.0510.0190.0000.0000.1010.0000.0000.0000.0390.0480.0000.0720.0020.031-0.0260.124-0.016-0.1160.0000.0680.0570.141-0.1250.0480.1350.154-0.0590.000-0.0380.0870.059-0.100-0.0160.0690.0880.0900.1840.0200.0250.000
BsmtFinType10.1980.1000.3270.1030.0960.2750.1911.0000.2140.4750.0820.0440.022-0.0580.1050.0340.2040.2100.2160.0660.2900.114-0.1040.2890.2230.0900.145-0.0020.0790.0690.2590.0650.2100.1160.0430.1990.049-0.1070.1540.1040.0690.2740.0850.058-0.0500.090-0.0520.0380.3060.0030.0480.182-0.0400.0590.0490.0330.0000.0500.0460.1610.097-0.0910.101-0.0460.193-0.057-0.1770.3440.0860.000-0.0160.0000.130
BsmtFinType20.0290.0530.0930.0000.0000.0490.4690.2141.0000.2720.0750.0160.0250.1130.0000.0180.0820.1260.1030.0000.0900.037-0.0580.1080.0460.0780.038-0.0180.1610.0310.0340.0230.0450.0540.0320.0750.000-0.1080.0530.0220.0000.0500.0000.000-0.067-0.004-0.0370.0310.1560.0130.1200.023-0.0680.0570.0800.1260.1070.0730.1110.0600.0030.0420.060-0.0550.0940.012-0.0600.0780.0930.120-0.0290.0000.037
BsmtUnfSF0.010-0.023-0.1570.1580.075-0.573-0.2700.4750.2721.000-0.1190.0160.0620.1390.0380.0050.1110.0940.1010.0260.2520.0870.2240.1700.1860.0460.095-0.0130.1800.0380.1590.0780.1790.2530.1220.0980.000-0.1280.1500.1220.0620.1920.0640.0120.0780.020-0.0450.0370.191-0.0170.2720.095-0.0370.0390.0540.1760.0000.0910.0000.2610.1310.1850.092-0.0130.0590.0130.3300.2640.0550.000-0.0240.0420.072
Building_Class-0.107-0.029-0.1140.0690.024-0.107-0.0820.0820.075-0.1191.0000.1030.1030.0360.1190.0430.1970.1880.2100.1150.2440.193-0.2780.2630.2490.0760.224-0.0190.0820.0440.3420.0720.2430.2030.5130.1680.105-0.0710.6170.8510.4760.2260.0840.062-0.2700.076-0.0330.0180.422-0.0270.1070.1760.0330.1380.0000.0060.1030.1170.0390.1660.1500.0070.086-0.0220.487-0.036-0.3180.2090.0860.000-0.0160.0000.264
Condition10.0400.0360.1520.0600.0000.1070.0000.0440.0160.0160.1031.0000.2100.2120.0400.0130.0710.0720.0790.0190.1250.0480.0660.0800.0620.0000.073-0.0040.1280.0000.1440.0000.0620.0780.0870.1570.000-0.0420.0850.0760.0670.0830.0000.1480.088-0.057-0.0190.0170.1850.0060.1610.103-0.0200.1050.0000.1720.1650.0810.0770.0640.0000.1870.0340.019-0.0000.0570.0920.0000.0000.000-0.0170.0000.071
Condition20.0680.0000.1100.0000.1030.3120.0400.0220.0250.0620.1030.2101.0000.0780.000-0.0160.0000.0220.0000.2840.1370.0000.0570.0350.1060.0000.0940.0330.0210.0000.0270.1630.0000.0270.1990.0690.000-0.0140.1220.1440.1220.0900.0590.0920.047-0.1010.0020.0020.009-0.0250.0370.0580.0000.0000.0000.0420.0000.3110.0000.0330.0000.0620.0000.0020.0020.0010.0740.0000.0000.0000.0440.0000.059
Construction_Year0.3410.086-0.680-0.0350.4040.190-0.111-0.0580.1130.1390.0360.2120.0781.0000.188-0.0000.1800.3350.3260.1890.4350.1690.2940.5020.3510.0760.2380.0130.7570.1800.4670.2250.3390.2880.2280.3360.171-0.4170.2910.2500.2140.4020.1590.1050.103-0.146-0.0920.0190.480-0.0490.6470.3450.0090.1740.0980.6840.0000.1610.0710.1770.1970.6530.157-0.0730.0290.0220.4270.1440.0890.0000.0120.0000.295
Electrical_System0.4210.4210.2060.0810.0000.0570.0000.1050.0000.0380.1190.0400.0000.1881.0000.0210.0660.2040.1800.1280.1380.0850.1230.1820.1160.2190.092-0.0100.2460.2270.1750.3370.1270.1420.0820.1440.1320.0450.1090.0810.1070.2020.0520.0070.078-0.0350.032-0.0040.1800.0220.2480.1930.0210.1120.0000.3270.0000.0000.0000.1000.1590.2960.000-0.0000.0460.0000.1590.0550.0000.0850.0430.0000.103
Enclosed_Lobby_Area-0.017-0.049-0.005-0.0100.032-0.004-0.0180.0340.0180.0050.0430.013-0.016-0.0000.0211.0000.0000.0140.0460.0000.0220.0140.0090.0760.0350.0000.0000.0060.0010.0000.0000.0000.0000.0080.0440.0000.000-0.0400.0340.0440.0300.0000.0360.059-0.018-0.0140.019-0.0260.0110.0160.0260.0000.0070.0360.010-0.0120.0000.0000.000-0.0090.0630.0210.0580.0510.0010.004-0.0070.0000.0000.000-0.0500.0360.000
Exposure_Level0.0930.0550.2010.1000.0980.2370.0650.2040.0820.1110.1970.0710.0000.1800.0660.0001.0000.1230.1420.0000.1590.132-0.2400.1350.0970.0390.131-0.007-0.2340.0570.1790.0490.159-0.0720.0620.0870.0000.0760.2270.0680.0500.1400.1920.068-0.1790.0700.028-0.0180.2700.008-0.2360.069-0.0460.1040.223-0.2230.0910.1310.144-0.0210.095-0.2920.109-0.0070.1330.008-0.2780.2050.0480.000-0.0050.0340.073
Exterior1st0.3490.0870.3210.0820.0000.1370.0710.2100.1260.0940.1880.0720.0220.3350.2040.0140.1231.0000.7590.0950.3510.145-0.0010.3140.2370.0940.1880.0140.1180.1100.3270.0910.2430.0690.1190.2660.134-0.0480.1600.1630.1570.2900.1160.0530.0660.033-0.0030.0020.2890.0170.1060.190-0.0060.0820.1340.1730.0000.1380.1860.0630.1730.0680.119-0.0250.058-0.0280.0040.0890.0660.0000.0080.0410.178
Exterior2nd0.3300.0710.3160.0670.0260.1390.0620.2160.1030.1010.2100.0790.0000.3260.1800.0460.1420.7591.0000.0670.3560.118-0.0020.3140.2250.0880.193-0.0060.0970.0940.3280.0900.2350.0670.1650.2650.174-0.0220.1670.1870.1300.2840.1210.0760.0490.019-0.007-0.0080.3180.0010.1060.174-0.0310.0940.1160.1550.0000.1600.1160.0650.1640.0700.117-0.0070.059-0.0160.0000.0820.0680.000-0.0080.0290.186
Exterior_Condition0.2000.2080.1080.0000.0000.0000.0000.0660.0000.0260.1150.0190.2840.1890.1280.0000.0000.0950.0671.0000.1820.0340.0600.1230.0750.1640.0000.0610.1530.0680.1150.1230.1290.0620.0510.0620.046-0.2270.1030.1100.0000.1780.0000.0000.033-0.049-0.0190.0200.153-0.0060.1290.154-0.0330.0000.0000.0780.0000.0920.0000.0330.0510.1280.090-0.0140.003-0.0000.1280.0000.0590.0000.0150.0110.079
Exterior_Material0.2780.1520.4640.1710.2400.2080.0430.2900.0900.2520.2440.1250.1370.4350.1380.0220.1590.3510.3560.1821.0000.186-0.3070.3720.3180.1270.2350.035-0.5760.0480.3960.0410.362-0.4170.1510.3240.0420.2640.1760.1760.0880.5470.1340.015-0.1310.0160.048-0.0410.4860.034-0.6720.193-0.0310.1120.094-0.5870.3210.1460.066-0.2770.236-0.6340.2600.031-0.163-0.045-0.3930.0700.0430.000-0.0430.0390.239
Fireplaces0.1960.0200.1800.1070.1540.2980.0800.1140.0370.0870.1930.0480.0000.1690.0850.0140.1320.1450.1180.0340.1861.0000.3950.1200.1790.0000.187-0.0020.0250.0240.2680.0520.2020.4810.1640.0970.000-0.0450.0990.1250.0860.1840.0660.0490.350-0.044-0.0080.0430.3050.0440.4200.1080.0840.1410.1540.1190.0590.0800.2700.3470.0850.5190.0770.1790.1880.0360.3260.1120.0000.0000.0390.0300.136
First_Floor_Area0.1610.012-0.2650.1410.3520.3230.067-0.104-0.0580.224-0.2780.0660.0570.2940.1230.009-0.240-0.001-0.0020.060-0.3070.3951.0000.0880.2580.0330.129-0.0090.1650.0000.2520.0260.2380.4950.1220.0940.000-0.1670.1600.1840.0410.2510.0960.0570.444-0.039-0.0330.0540.2410.0260.4090.1040.0710.2070.0000.2410.0000.1510.3940.3620.1230.5760.0880.108-0.2760.0600.8290.1910.0000.0000.0350.0090.160
Foundation_Type0.3650.1370.4080.0860.0750.1110.0690.2890.1080.1700.2630.0800.0350.5020.1820.0760.1350.3140.3140.1230.3720.1200.0881.0000.2840.1010.219-0.0270.5720.1320.3780.2000.2690.2890.1640.2930.216-0.3690.2160.1880.1670.3430.1000.0420.051-0.068-0.0590.0010.417-0.0530.4820.2360.0010.1160.0500.5010.0440.0920.0000.1950.1580.4920.150-0.0820.1200.0260.2400.1030.0670.000-0.0040.0330.224
Full_Bathroom_Above_Grade0.1030.1440.3370.4480.1790.1580.0350.2230.0460.1860.2490.0620.1060.3510.1160.0350.0970.2370.2250.0750.3180.1790.2580.2841.0000.1160.247-0.0390.4630.0520.3280.0560.3280.6580.2310.1990.000-0.2620.2360.1050.1120.2770.1110.0410.2360.000-0.0490.0670.369-0.0160.5760.0990.0420.1020.1240.4310.0220.1390.1060.5590.1830.6360.132-0.0370.3840.0370.3290.2640.1640.0000.0230.0000.175
Functional_Rate0.0890.2190.1090.0160.0260.0000.1090.0900.0780.0460.0760.0000.0000.0760.2190.0000.0390.0940.0880.1640.1270.0000.0330.1010.1161.0000.1540.0360.1480.0600.0890.1170.049-0.0730.0380.0080.0580.0560.0270.0500.0000.1010.0690.000-0.040-0.043-0.0990.0230.095-0.0330.1760.0730.0190.0670.1120.1000.0000.1250.142-0.0440.0110.1340.000-0.0390.0070.0140.0370.0000.0000.000-0.0070.0480.000
Garage0.2310.0970.2460.1310.0700.1060.0350.1450.0380.0950.2240.0730.0940.2380.0920.0000.1310.1880.1930.0000.2350.1870.1290.2190.2470.1541.0000.014-0.3330.1440.4110.1370.217-0.1750.2040.1490.1100.1580.1860.1280.1260.1940.1010.044-0.2120.0960.0190.0160.2560.007-0.2890.146-0.0270.1290.113-0.2340.2320.0550.000-0.1110.107-0.3690.074-0.0090.120-0.054-0.3720.0940.0000.219-0.0450.0000.189
Garage_Area-0.014-0.031-0.003-0.0360.0160.0200.013-0.002-0.018-0.013-0.019-0.0040.0330.013-0.0100.006-0.0070.014-0.0060.0610.035-0.002-0.009-0.027-0.0390.0360.0141.000-0.0080.0610.0670.0220.082-0.0560.0670.0000.000-0.0110.0000.0040.1120.0660.0290.0100.019-0.061-0.0090.0170.0000.0340.0040.0000.0130.0000.0560.0040.0000.0590.000-0.0540.040-0.0030.0480.026-0.0430.0040.0160.0000.0940.065-0.0060.0000.000
Garage_Built_Year0.1490.025-0.572-0.0610.2440.031-0.1610.0790.1610.1800.0820.1280.0210.7570.2460.001-0.2340.1180.0970.153-0.5760.0250.1650.5720.4630.148-0.333-0.0081.0000.2580.3360.2600.3670.2070.1760.3010.107-0.3430.2160.1440.1720.3320.1110.094-0.0060.013-0.0630.0080.394-0.0570.4930.205-0.0120.1400.0640.6450.0000.1230.0300.1560.1910.4520.153-0.1160.0450.0070.2650.0930.0770.0000.0010.0000.191
Garage_Condition0.2380.1880.1490.0000.0000.0000.0000.0690.0310.0380.0440.0000.0000.1800.2270.0000.0570.1100.0940.0680.0480.0240.0000.1320.0520.0600.1440.0610.2581.0000.1180.6090.0720.0600.0290.0650.167-0.0470.1240.0000.1490.0880.0000.0510.031-0.029-0.022-0.0060.111-0.0080.1430.180-0.0390.0280.0000.1530.0000.0450.0700.0110.0240.1710.0000.033-0.025-0.0040.1220.0150.0000.000-0.0070.0000.065
Garage_Finish_Year0.2670.1080.4170.1090.1980.2090.0230.2590.0340.1590.3420.1440.0270.4670.1750.0000.1790.3270.3280.1150.3960.2680.2520.3780.3280.0890.4110.0670.3360.1181.0000.1330.364-0.3790.1870.2920.0810.2260.2430.2010.1310.3640.1250.046-0.2090.0870.0190.0060.4860.009-0.5570.179-0.0310.1750.000-0.4720.0000.1140.021-0.2640.196-0.6180.188-0.022-0.118-0.044-0.3680.1180.0040.018-0.0310.0000.231
Garage_Quality0.1860.2350.1660.0410.0000.0270.0000.0650.0230.0780.0720.0000.1630.2250.3370.0000.0490.0910.0900.1230.0410.0520.0260.2000.0560.1170.1370.0220.2600.6090.1331.0000.0960.0180.0000.0600.114-0.1030.1420.0530.0550.0900.0000.0100.038-0.021-0.0460.0090.1550.0000.0950.141-0.0300.0620.0000.1240.0000.0000.102-0.0010.0040.1380.000-0.009-0.057-0.0220.1000.0320.0000.000-0.0200.0270.097
Garage_Size0.2830.0990.3980.1340.1990.1760.0000.2100.0450.1790.2430.0620.0000.3390.1270.0000.1590.2430.2350.1290.3620.2020.2380.2690.3280.0490.2170.0820.3670.0720.3640.0961.0000.5050.1980.1800.090-0.2540.1640.1540.1230.3630.0900.0440.341-0.074-0.0580.0400.392-0.0210.6090.2630.0220.1190.0330.4560.0270.1330.0000.3860.2130.6910.1910.0260.1560.0350.4560.1160.0720.000-0.0000.0000.145
Grade_Living_Area0.104-0.022-0.3620.5430.3230.058-0.0510.1160.0540.2530.2030.0780.0270.2880.1420.008-0.0720.0690.0670.062-0.4170.4810.4950.2890.658-0.073-0.175-0.0560.2070.060-0.3790.0180.5051.0000.3010.1430.052-0.1530.2580.0490.0000.2660.1000.0460.4490.064-0.0490.0810.209-0.0100.6030.0940.0680.2210.0360.2820.0000.0620.4060.8280.0840.7310.0350.0850.6430.0340.3710.1360.0000.0000.0300.0420.106
Half_Bathroom_Above_Grade0.1300.0400.1660.2500.1370.0120.0190.0430.0320.1220.5130.0870.1990.2280.0820.0440.0620.1190.1650.0510.1510.1640.1220.1640.2310.0380.2040.0670.1760.0290.1870.0000.1980.3011.0000.0970.000-0.0720.4620.2290.1920.1480.0000.0040.143-0.019-0.033-0.0050.301-0.0270.3000.0850.0270.0850.0410.1520.0000.2100.0190.3600.1300.3430.0360.0590.626-0.001-0.1050.1520.1540.0000.0130.0000.140
Heating_Quality0.3790.0870.2740.0210.0350.0590.0000.1990.0750.0980.1680.1570.0690.3360.1440.0000.0870.2660.2650.0620.3240.0970.0940.2930.1990.0080.1490.0000.3010.0650.2920.0600.1800.1430.0971.0000.2390.1040.1680.1120.0960.3180.0540.009-0.0690.0200.033-0.0060.2970.055-0.4590.1750.0270.0530.050-0.5300.0180.0000.000-0.1960.149-0.4710.1320.059-0.128-0.066-0.2420.0610.0300.027-0.0180.0040.117
Heating_Type0.4610.0600.0250.0610.0000.0000.0000.0490.0000.0000.1050.0000.0000.1710.1320.0000.0000.1340.1740.0460.0420.0000.0000.2160.0000.0580.1100.0000.1070.1670.0810.1140.0900.0520.0000.2391.000-0.0620.1410.1070.0890.1550.0000.000-0.0030.017-0.0020.0020.0540.002-0.0790.147-0.0100.0250.000-0.1360.0000.0000.0000.0110.000-0.1060.0640.0260.054-0.018-0.0690.0000.0000.0000.0420.0280.055
House_Condition0.099-0.0090.288-0.004-0.180-0.0120.101-0.107-0.108-0.128-0.071-0.042-0.014-0.4170.045-0.0400.076-0.048-0.022-0.2270.264-0.045-0.167-0.369-0.2620.0560.158-0.011-0.343-0.0470.226-0.103-0.254-0.153-0.0720.104-0.0621.0000.1220.1270.0730.2460.1010.000-0.0470.0400.087-0.0070.2230.011-0.1770.186-0.0060.0590.188-0.0410.0680.0440.000-0.1050.116-0.1290.1040.0750.0020.032-0.2180.0000.1020.0000.0380.0490.161
House_Design0.2330.0840.2110.2420.0460.1140.0000.1540.0530.1500.6170.0850.1220.2910.1090.0340.2270.1600.1670.1030.1760.0990.1600.2160.2360.0270.1860.0000.2160.1240.2430.1420.1640.2580.4620.1680.1410.1221.0000.1560.1500.1470.1260.0000.061-0.054-0.0510.0240.294-0.0050.2470.1630.0720.0730.0000.1740.0190.1020.0470.2600.0860.2530.055-0.0420.465-0.019-0.1690.1640.0990.100-0.0490.0000.184
House_Type0.2890.0380.1700.3020.0000.0000.0000.1040.0220.1220.8510.0760.1440.2500.0810.0440.0680.1630.1870.1100.1760.1250.1840.1880.1050.0500.1280.0040.1440.0000.2010.0530.1540.0490.2290.1120.1070.1270.1561.0000.4880.1500.0690.069-0.4260.008-0.040-0.0330.419-0.025-0.0060.147-0.0310.0850.0260.0460.1120.0480.027-0.1630.153-0.1200.098-0.037-0.063-0.026-0.0630.1600.0650.000-0.0030.0000.188
Kitchen_Above_Grade0.2440.0610.0910.2330.0000.0000.0000.0690.0000.0620.4760.0670.1220.2140.1070.0300.0500.1570.1300.0000.0880.0860.0410.1670.1120.0000.1260.1120.1720.1490.1310.0550.1230.0000.1920.0960.0890.0730.1500.4881.0000.1020.0000.041-0.023-0.0040.0300.0280.1020.034-0.1930.135-0.0150.0380.000-0.1540.0000.1600.1750.2220.322-0.1650.000-0.0520.054-0.028-0.0250.1290.4980.000-0.0260.0000.091
Kitchen_Quality0.3430.1170.4210.1310.1870.2090.0390.2740.0500.1920.2260.0830.0900.4020.2020.0000.1400.2900.2840.1780.5470.1840.2510.3430.2770.1010.1940.0660.3320.0880.3640.0900.3630.2660.1480.3180.1550.2460.1470.1500.1021.0000.0970.000-0.1290.0280.069-0.0500.4440.053-0.5730.191-0.0570.0920.045-0.5580.0620.1110.038-0.2430.212-0.5700.2090.008-0.150-0.028-0.3260.0950.0000.000-0.0560.0000.174
Land_Outline0.1280.0610.0940.1100.0250.1380.0480.0850.0000.0640.0840.0000.0590.1590.0520.0360.1920.1160.1210.0000.1340.0660.0960.1000.1110.0690.1010.0290.1110.0000.1250.0000.0900.1000.0000.0540.0000.1010.1260.0690.0000.0971.0000.060-0.081-0.0460.006-0.0300.3600.0020.0180.116-0.0110.1260.4570.0630.1140.1410.181-0.0170.107-0.0100.030-0.008-0.022-0.0600.0140.1050.0270.000-0.0460.0000.102
Lot_Configuration0.0630.0290.0850.0000.0360.0460.0000.0580.0000.0120.0620.1480.0920.1050.0070.0590.0680.0530.0760.0000.0150.0490.0570.0420.0410.0000.0440.0100.0940.0510.0460.0100.0440.0460.0040.0090.0000.0000.0000.0690.0410.0000.0601.000-0.196-0.005-0.0220.0240.1370.003-0.0320.029-0.0480.2210.0790.0030.0000.0750.077-0.0400.034-0.0740.0000.010-0.043-0.018-0.0360.0000.0340.085-0.0300.0310.064
Lot_Size0.1060.001-0.1350.3380.1770.1720.072-0.050-0.0670.078-0.2700.0880.0470.1030.078-0.018-0.1790.0660.0490.033-0.1310.3500.4440.0510.236-0.040-0.2120.019-0.0060.031-0.2090.0380.3410.4490.143-0.069-0.003-0.0470.061-0.426-0.023-0.129-0.081-0.1961.000-0.0200.0590.0060.1620.0060.2340.0300.0840.2660.4490.0750.2900.1120.2520.4060.0000.4570.0000.0920.1200.0620.3660.2110.0000.0000.0240.0000.000
LowQualFinSF-0.049-0.0430.0830.021-0.107-0.0790.0020.090-0.0040.0200.076-0.057-0.101-0.146-0.035-0.0140.0700.0330.019-0.0490.016-0.044-0.039-0.0680.000-0.0430.096-0.0610.013-0.0290.087-0.021-0.0740.064-0.0190.0200.0170.040-0.0540.008-0.0040.028-0.046-0.005-0.0201.0000.029-0.0040.112-0.027-0.0340.0920.0660.0000.053-0.0650.0000.0000.0490.0420.000-0.0680.000-0.0190.0580.022-0.0810.0000.0000.000-0.0120.0210.144
Miscellaneous_Value-0.0090.0130.0590.013-0.0510.0050.031-0.052-0.037-0.045-0.033-0.0190.002-0.0920.0320.0190.028-0.003-0.007-0.0190.048-0.008-0.033-0.059-0.049-0.0990.019-0.009-0.063-0.0220.019-0.046-0.058-0.049-0.0330.033-0.0020.087-0.051-0.0400.0300.0690.006-0.0220.0590.0291.0000.0110.0000.038-0.0880.0890.0420.0000.000-0.0910.0000.3510.000-0.0220.000-0.0630.0000.015-0.0050.005-0.0610.0000.0000.000-0.0050.0210.000
Month_Sold0.015-0.031-0.0110.0510.018-0.016-0.0260.0380.0310.0370.0180.0170.0020.019-0.004-0.026-0.0180.002-0.0080.020-0.0410.0430.0540.0010.0670.0230.0160.0170.008-0.0060.0060.0090.0400.081-0.005-0.0060.002-0.0070.024-0.0330.028-0.050-0.0300.0240.006-0.0040.0111.0000.053-0.0160.0610.000-0.0230.0000.0450.0210.0570.0000.0000.0400.0520.0690.0310.0240.0430.0370.0300.0000.0000.0470.0160.1550.024
Neighborhood0.3830.1180.5330.2060.1820.2010.1240.3060.1560.1910.4220.1850.0090.4800.1800.0110.2700.2890.3180.1530.4860.3050.2410.4170.3690.0950.2560.0000.3940.1110.4860.1550.3920.2090.3010.2970.0540.2230.2940.4190.1020.4440.3600.1370.1620.1120.0000.0531.000-0.0460.1720.310-0.0090.2440.3150.1020.1990.1860.0950.0910.2200.1660.169-0.0100.022-0.0340.1430.1900.1450.0960.0040.0000.641
Open_Lobby_Area0.012-0.0220.0490.001-0.0010.021-0.0160.0030.013-0.017-0.0270.006-0.025-0.0490.0220.0160.0080.0170.001-0.0060.0340.0440.026-0.053-0.016-0.0330.0070.034-0.057-0.0080.0090.000-0.021-0.010-0.0270.0550.0020.011-0.005-0.0250.0340.0530.0020.0030.006-0.0270.038-0.016-0.0461.000-0.0430.0000.0180.0330.029-0.0570.0000.0660.0560.0020.034-0.0180.0000.060-0.030-0.012-0.0050.0000.0000.022-0.0190.0330.000
Overall_Material0.2480.015-0.6030.1220.4140.134-0.1160.0480.1200.2720.1070.1610.0370.6470.2480.026-0.2360.1060.1060.129-0.6720.4200.4090.4820.5760.176-0.2890.0040.4930.143-0.5570.0950.6090.6030.300-0.459-0.079-0.1770.247-0.006-0.193-0.5730.018-0.0320.234-0.034-0.0880.0610.172-0.0431.0000.1760.0570.1160.1520.5570.0730.1170.0990.4280.1520.8100.1620.0460.2890.0330.4610.0660.0620.0000.0330.0000.190
Pavedd_Drive0.3360.1370.1800.0980.0750.1090.0000.1820.0230.0950.1760.1030.0580.3450.1930.0000.0690.1900.1740.1540.1930.1080.1040.2360.0990.0730.1460.0000.2050.1800.1790.1410.2630.0940.0850.1750.1470.1860.1630.1470.1350.1910.1160.0290.0300.0920.0890.0000.3100.0000.1761.0000.0210.0760.0000.1810.0000.1170.0000.0040.1060.2810.0700.044-0.0540.0190.2300.0860.0160.000-0.0380.0000.218
Pool_Area0.0180.020-0.0180.0720.0040.0580.068-0.040-0.068-0.0370.033-0.0200.0000.0090.0210.007-0.046-0.006-0.031-0.033-0.0310.0840.0710.0010.0420.019-0.0270.013-0.012-0.039-0.031-0.0300.0220.0680.0270.027-0.010-0.0060.072-0.031-0.015-0.057-0.011-0.0480.0840.0660.042-0.023-0.0090.0180.0570.0211.0000.1210.0000.0030.0000.1290.3780.0590.1410.0580.0000.0190.061-0.0090.0470.0990.0000.000-0.0050.0000.000
Property_Shape0.1080.0450.1390.0330.0700.2060.0570.0590.0570.0390.1380.1050.0000.1740.1120.0360.1040.0820.0940.0000.1120.1410.2070.1160.1020.0670.1290.0000.1400.0280.1750.0620.1190.2210.0850.0530.0250.0590.0730.0850.0380.0920.1260.2210.2660.0000.0000.0000.2440.0330.1160.0760.1211.0000.119-0.1310.0340.0350.186-0.1190.002-0.3060.000-0.047-0.047-0.036-0.1830.0950.0450.000-0.0440.0000.152
Property_Slope0.0000.1350.0000.1000.0000.0830.1410.0490.0800.0540.0000.0000.0000.0980.0000.0100.2230.1340.1160.0000.0940.1540.0000.0500.1240.1120.1130.0560.0640.0000.0000.0000.0330.0360.0410.0500.0000.1880.0000.0260.0000.0450.4570.0790.4490.0530.0000.0450.3150.0290.1520.0000.0000.1191.000-0.0670.1760.2560.313-0.0330.0380.0500.0000.066-0.0020.0180.0240.2000.0450.0000.0070.0000.072
Remodel_Year0.2750.007-0.526-0.0540.2360.064-0.1250.0330.1260.1760.0060.1720.0420.6840.327-0.012-0.2230.1730.1550.078-0.5870.1190.2410.5010.4310.100-0.2340.0040.6450.153-0.4720.1240.4560.2820.152-0.530-0.136-0.0410.1740.046-0.154-0.5580.0630.0030.075-0.065-0.0910.0210.102-0.0570.5570.1810.003-0.131-0.0671.0000.1110.0810.0400.1980.2590.5710.207-0.0460.0730.0520.3000.1220.0770.0790.0410.0000.202
Road_Type0.0400.0000.0000.0000.0000.0190.0480.0000.1070.0000.1030.1650.0000.0000.0000.0000.0910.0000.0000.0000.3210.0590.0000.0440.0220.0000.2320.0000.0000.0000.0000.0000.0270.0000.0000.0180.0000.0680.0190.1120.0000.0620.1140.0000.2900.0000.0000.0570.1990.0000.0730.0000.0000.0340.1760.1111.0000.0000.0000.0520.0990.0460.111-0.0230.0420.0080.0100.0910.0000.000-0.0170.0350.249
Roof_Design0.0550.0510.1640.1400.1050.0960.1350.0500.0730.0910.1170.0810.3110.1610.0000.0000.1310.1380.1600.0920.1460.0800.1510.0920.1390.1250.0550.0590.1230.0450.1140.0000.1330.0620.2100.0000.0000.0440.1020.0480.1600.1110.1410.0750.1120.0000.3510.0000.1860.0660.1170.1170.1290.0350.2560.0810.0001.0000.4580.1160.0880.1400.0000.063-0.0960.0180.2250.1250.1270.000-0.0000.0000.073
Roof_Quality0.0000.0400.0400.1010.1420.4490.1540.0460.1110.0000.0390.0770.0000.0710.0000.0000.1440.1860.1160.0000.0660.2700.3940.0000.1060.1420.0000.0000.0300.0700.0210.1020.0000.4060.0190.0000.0000.0000.0470.0270.1750.0380.1810.0770.2520.0490.0000.0000.0950.0560.0990.0000.3780.1860.3130.0400.0000.4581.0000.0150.0570.0750.0000.090-0.012-0.0520.0400.1660.1390.0000.0530.0000.000
Rooms_Above_Grade0.044-0.018-0.2170.6680.263-0.050-0.0590.1610.0600.2610.1660.0640.0330.1770.100-0.009-0.0210.0630.0650.033-0.2770.3470.3620.1950.559-0.044-0.111-0.0540.1560.011-0.264-0.0010.3860.8280.360-0.1960.011-0.1050.260-0.1630.222-0.243-0.017-0.0400.4060.042-0.0220.0400.0910.0020.4280.0040.059-0.119-0.0330.1980.0520.1160.0151.0000.0860.5330.0470.0320.587-0.0030.2340.0630.0000.0000.0210.0000.175
Sale_Condition0.1130.0640.2450.1050.0560.0820.0000.0970.0030.1310.1500.0000.0000.1970.1590.0630.0950.1730.1640.0510.2360.0850.1230.1580.1830.0110.1070.0400.1910.0240.1960.0040.2130.0840.1300.1490.0000.1160.0860.1530.3220.2120.1070.0340.0000.0000.0000.0520.2200.0340.1520.1060.1410.0020.0380.2590.0990.0880.0570.0861.0000.3200.471-0.0200.0280.0270.1840.2070.2540.0760.0450.0800.136
Sale_Price0.3130.032-0.5910.2350.4230.302-0.038-0.0910.0420.1850.0070.1870.0620.6530.2960.021-0.2920.0680.0700.128-0.6340.5190.5760.4920.6360.134-0.369-0.0030.4520.171-0.6180.1380.6910.7310.343-0.471-0.106-0.1290.253-0.120-0.165-0.570-0.010-0.0740.457-0.068-0.0630.0690.166-0.0180.8100.2810.058-0.3060.0500.5710.0460.1400.0750.5330.3201.0000.1280.1000.2940.0650.6030.1410.0490.0000.0420.0000.207
Sale_Type0.1280.0850.2460.0570.0370.0720.0870.1010.0600.0920.0860.0340.0000.1570.0000.0580.1090.1190.1170.0900.2600.0770.0880.1500.1320.0000.0740.0480.1530.0000.1880.0000.1910.0350.0360.1320.0640.1040.0550.0980.0000.2090.0300.0000.0000.0000.0000.0310.1690.0000.1620.0700.0000.0000.0000.2070.1110.0000.0000.0470.4710.1281.0000.0230.038-0.028-0.1830.1150.0070.131-0.0340.0850.151
Screen_Lobby_Area0.0390.0080.0230.0340.0370.0720.059-0.046-0.055-0.013-0.0220.0190.002-0.073-0.0000.051-0.007-0.025-0.007-0.0140.0310.1790.108-0.082-0.037-0.039-0.0090.026-0.1160.033-0.022-0.0090.0260.0850.0590.0590.0260.075-0.042-0.037-0.0520.008-0.0080.0100.092-0.0190.0150.024-0.0100.0600.0460.0440.019-0.0470.066-0.046-0.0230.0630.0900.032-0.0200.1000.0231.0000.012-0.0380.0890.0000.0000.214-0.0090.0000.000
Second_Floor_Area-0.020-0.040-0.1390.5110.063-0.191-0.1000.1930.0940.0590.487-0.0000.0020.0290.0460.0010.1330.0580.0590.003-0.1630.188-0.2760.1200.3840.0070.120-0.0430.045-0.025-0.118-0.0570.1560.6430.626-0.1280.0540.0020.465-0.0630.054-0.150-0.022-0.0430.1200.058-0.0050.0430.022-0.0300.289-0.0540.061-0.047-0.0020.0730.042-0.096-0.0120.5870.0280.2940.0380.0121.000-0.023-0.2860.1250.0000.000-0.0000.0550.159
Three_Season_Lobby_Area0.0340.037-0.023-0.0190.0400.047-0.016-0.0570.0120.013-0.0360.0570.0010.0220.0000.0040.008-0.028-0.016-0.000-0.0450.0360.0600.0260.0370.014-0.0540.0040.007-0.004-0.044-0.0220.0350.034-0.001-0.066-0.0180.032-0.019-0.026-0.028-0.028-0.060-0.0180.0620.0220.0050.037-0.034-0.0120.0330.019-0.009-0.0360.0180.0520.0080.018-0.052-0.0030.0270.065-0.028-0.038-0.0231.0000.0490.0000.0580.000-0.0160.0000.000
Total_Basement_Area0.2160.030-0.3520.0590.3610.4100.069-0.177-0.0600.330-0.3180.0920.0740.4270.159-0.007-0.2780.0040.0000.128-0.3930.3260.8290.2400.3290.037-0.3720.0160.2650.122-0.3680.1000.4560.371-0.105-0.242-0.069-0.218-0.169-0.063-0.025-0.3260.014-0.0360.366-0.081-0.0610.0300.143-0.0050.4610.2300.047-0.1830.0240.3000.0100.2250.0400.2340.1840.603-0.1830.089-0.2860.0491.0000.2040.0000.0000.0310.0000.119
Underground_Full_Bathroom0.1060.0750.1100.2550.0250.3970.0880.3440.0780.2640.2090.0000.0000.1440.0550.0000.2050.0890.0820.0000.0700.1120.1910.1030.2640.0000.0940.0000.0930.0150.1180.0320.1160.1360.1520.0610.0000.0000.1640.1600.1290.0950.1050.0000.2110.0000.0000.0000.1900.0000.0660.0860.0990.0950.2000.1220.0910.1250.1660.0630.2070.1410.1150.0000.1250.0000.2041.0000.0970.0000.0210.0510.071
Underground_Half_Bathroom0.0160.0600.0470.0300.0000.0280.0900.0860.0930.0550.0860.0000.0000.0890.0000.0000.0480.0660.0680.0590.0430.0000.0000.0670.1640.0000.0000.0940.0770.0000.0040.0000.0720.0000.1540.0300.0000.1020.0990.0650.4980.0000.0270.0340.0000.0000.0000.0000.1450.0000.0620.0160.0000.0450.0450.0770.0000.1270.1390.0000.2540.0490.0070.0000.0000.0580.0000.0971.0000.1020.0010.0240.020
Utility_Type0.0000.0000.0000.0000.1700.0000.1840.0000.1200.0000.0000.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2190.0650.0000.0000.0180.0000.0000.0000.0000.0270.0000.0000.1000.0000.0000.0000.0000.0850.0000.0000.0000.0470.0960.0220.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0760.0000.1310.2140.0000.0000.0000.0000.1021.000-0.0050.0000.000
W_Deck_Area-0.024-0.028-0.0210.008-0.0060.0370.020-0.016-0.029-0.024-0.016-0.0170.0440.0120.043-0.050-0.0050.008-0.0080.015-0.0430.0390.035-0.0040.023-0.007-0.045-0.0060.001-0.007-0.031-0.020-0.0000.0300.013-0.0180.0420.038-0.049-0.003-0.026-0.056-0.046-0.0300.024-0.012-0.0050.0160.004-0.0190.033-0.038-0.005-0.0440.0070.041-0.017-0.0000.0530.0210.0450.042-0.034-0.009-0.000-0.0160.0310.0210.001-0.0051.0000.0000.000
Year_Sold0.0000.0530.0000.0210.0390.0000.0250.0000.0000.0420.0000.0000.0000.0000.0000.0360.0340.0410.0290.0110.0390.0300.0090.0330.0000.0480.0000.0000.0000.0000.0000.0270.0000.0420.0000.0040.0280.0490.0000.0000.0000.0000.0000.0310.0000.0210.0210.1550.0000.0330.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0800.0000.0850.0000.0550.0000.0000.0510.0240.0000.0001.0000.000
Zoning_Class0.2970.0920.1890.1650.0610.0910.0000.1300.0370.0720.2640.0710.0590.2950.1030.0000.0730.1780.1860.0790.2390.1360.1600.2240.1750.0000.1890.0000.1910.0650.2310.0970.1450.1060.1400.1170.0550.1610.1840.1880.0910.1740.1020.0640.0000.1440.0000.0240.6410.0000.1900.2180.0000.1520.0720.2020.2490.0730.0000.1750.1360.2070.1510.0000.1590.0000.1190.0710.0200.0000.0000.0001.000

Missing values

2024-07-11T03:22:50.359231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-11T03:22:51.162277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Building_ClassZoning_ClassLot_SizeRoad_TypeProperty_ShapeLand_OutlineUtility_TypeLot_ConfigurationProperty_SlopeNeighborhoodCondition1Condition2House_TypeHouse_DesignOverall_MaterialHouse_ConditionConstruction_YearRemodel_YearRoof_DesignRoof_QualityExterior1stExterior2ndBrick_Veneer_AreaExterior_MaterialExterior_ConditionFoundation_TypeBasement_HeightBasement_ConditionExposure_LevelBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotal_Basement_AreaHeating_TypeHeating_QualityAir_ConditioningElectrical_SystemFirst_Floor_AreaSecond_Floor_AreaLowQualFinSFGrade_Living_AreaUnderground_Full_BathroomUnderground_Half_BathroomFull_Bathroom_Above_GradeHalf_Bathroom_Above_GradeBedroom_Above_GradeKitchen_Above_GradeKitchen_QualityRooms_Above_GradeFunctional_RateFireplacesGarageGarage_Built_YearGarage_Finish_YearGarage_SizeGarage_AreaGarage_QualityGarage_ConditionPavedd_DriveW_Deck_AreaOpen_Lobby_AreaEnclosed_Lobby_AreaThree_Season_Lobby_AreaScreen_Lobby_AreaPool_AreaMiscellaneous_ValueMonth_SoldYear_SoldSale_TypeSale_ConditionSale_Price
060RLD8450PavedRegLvlAllPubIGSCollgCrNormNorm1Fam2Story7520032003GableSSVinylSdVinylSd196.0GdTAPCGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8TF0Attchd2003RFn21085.793744TATAY163.78808069.59611520.337934000022008WDNormal208500
120RLD9600PavedRegLvlAllPubFR2PGSVeenkerFeedrNorm1Fam1Story6819761976GableSSMetalSdMetalSd0.0TATACBGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6TF1Attchd1976RFn2196.316304TATAY198.90007474.71603315.039392000052007WDNormal181500
260RLD11250PavedIR1LvlAllPubIGSCollgCrNormNorm1Fam2Story7520012002GableSSVinylSdVinylSd162.0GdTAPCGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6TF1Attchd2001RFn2218.068403TATAY26.12753332.085268-46.232198000092008WDNormal223500
370RLD9550PavedIR1LvlAllPubCGSCrawforNormNorm1Fam2Story7519151970GableSSWd SdngWd Shng0.0TATABTTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7TF1Detchd1998Unf3696.996439TATAY46.94801840.18141560.921821000022006WDAbnorml140000
460RLD14260PavedIR1LvlAllPubFR2PGSNoRidgeNormNorm1Fam2Story8520002000GableSSVinylSdVinylSd350.0GdTAPCGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9TF1Attchd2000RFn3568.859882TATAY-10.62610520.75532321.7888180000122008WDNormal250000
550RLD14115PavedIR1LvlAllPubIGSMitchelNormNorm1Fam1.5Fin5519931995GableSSVinylSdVinylSd0.0TATAWGdTANoGLQ732Unf064796GasAExYSBrkr79656601362101111TA5TF0Attchd1993Unf2703.481359TATAY0.62140236.74033570.35036232000700102009WDNormal143000
620RLD10084PavedRegLvlAllPubIGSSomerstNormNorm1Fam1Story8520042005GableSSVinylSdVinylSd186.0GdTAPCExTAAvGLQ1369Unf03171686GasAExYSBrkr1694001694102031Gd7TF1Attchd2004RFn2555.415694TATAY39.047177118.613457-7.064622000082007WDNormal307000
760RLD10382PavedIR1LvlAllPubCGSNWAmesPosNNorm1Fam2Story7619731973GableSSHdBoardHdBoard240.0TATACBGdTAMnALQ859BLQ322161107GasAExYSBrkr110798302090102131TA7TF2Attchd1973RFn2737.632993TATAY201.101046150.62150776.923944000350112009WDNormal200000
850RMD6120PavedRegLvlAllPubIGSOldTownArteryNorm1Fam1.5Fin7519311950GableSSBrkFaceWd Shng0.0TATABTTATANoUnf0Unf0952952GasAGdYFuseF102275201774002022TA8MD12Detchd1931Unf2424.620043FaTAY-24.04468457.93398678.563161000042008WDAbnorml129900
9190RLD7420PavedRegLvlAllPubCGSBrkSideArteryArtery2fmCon1.5Unf5619391950GableSSMetalSdMetalSd0.0TATABTTATANoGLQ851Unf0140991GasAExYSBrkr1077001077101022TA5TF2Attchd1939RFn1632.539240GdTAY31.12321959.82769657.757631000012008WDNormal118000
Building_ClassZoning_ClassLot_SizeRoad_TypeProperty_ShapeLand_OutlineUtility_TypeLot_ConfigurationProperty_SlopeNeighborhoodCondition1Condition2House_TypeHouse_DesignOverall_MaterialHouse_ConditionConstruction_YearRemodel_YearRoof_DesignRoof_QualityExterior1stExterior2ndBrick_Veneer_AreaExterior_MaterialExterior_ConditionFoundation_TypeBasement_HeightBasement_ConditionExposure_LevelBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotal_Basement_AreaHeating_TypeHeating_QualityAir_ConditioningElectrical_SystemFirst_Floor_AreaSecond_Floor_AreaLowQualFinSFGrade_Living_AreaUnderground_Full_BathroomUnderground_Half_BathroomFull_Bathroom_Above_GradeHalf_Bathroom_Above_GradeBedroom_Above_GradeKitchen_Above_GradeKitchen_QualityRooms_Above_GradeFunctional_RateFireplacesGarageGarage_Built_YearGarage_Finish_YearGarage_SizeGarage_AreaGarage_QualityGarage_ConditionPavedd_DriveW_Deck_AreaOpen_Lobby_AreaEnclosed_Lobby_AreaThree_Season_Lobby_AreaScreen_Lobby_AreaPool_AreaMiscellaneous_ValueMonth_SoldYear_SoldSale_TypeSale_ConditionSale_Price
1449180RMD1533PavedRegLvlAllPubIGSMeadowVNormNormTwnhsSFoyer5719701970GableSSCemntBdCmentBd0.0TATACBGdTAAvGLQ553Unf077630GasAExYSBrkr63000630101011Ex3TF0Attchd2005Unf0349.820761TATAY168.315750-13.5510884.360198000082006WDAbnorml92000
145090RLD9000PavedRegLvlAllPubFR2PGSNAmesNormNormDuplex2Story5519741974GableSSVinylSdVinylSd0.0TATACBGdTANoUnf0Unf0896896GasATAYSBrkr89689601792002242TA8TF0Attchd2005Unf0334.367666TATAY23.163353-35.044491-49.357801000092009WDNormal136000
145120RLD9262PavedRegLvlAllPubIGSSomerstNormNorm1Fam1Story8520082009GableSSCemntBdCmentBd194.0GdTAPCGdTANoUnf0Unf015731573GasAExYSBrkr1578001578002031Ex7TF1Attchd2008Fin3763.660221TATAY20.020463-25.811734-20.252731000052009NewPartial287090
1452180RMD3675PavedRegLvlAllPubIGSEdwardsNormNormTwnhsESLvl5520052005GableSSVinylSdVinylSd80.0TATAPCGdTAGdGLQ547Unf00547GasAGdYSBrkr1072001072101021TA5TF0Basment2005Fin2514.423635TATAY152.5134996.812704-4.014572000052006WDNormal145000
145320RLD17217PavedRegLvlAllPubIGSMitchelNormNorm1Fam1Story5520062006GableSSVinylSdVinylSd0.0TATAPCGdTANoUnf0Unf011401140GasAExYSBrkr1140001140001031TA6TF0Attchd2005Unf0538.606473TATAY53.845287-5.81986499.422640000072006WDAbnorml84500
145420FVR7500PavedRegLvlAllPubIGSSomerstNormNorm1Fam1Story7520042005GableSSVinylSdVinylSd0.0GdTAPCGdTANoGLQ410Unf08111221GasAExYSBrkr1221001221102021Gd6TF0Attchd2004RFn2689.060909TATAY-9.973961-9.267967126.6765470000102009WDNormal185000
145560RLD7917PavedRegLvlAllPubIGSGilbertNormNorm1Fam2Story6519992000GableSSVinylSdVinylSd0.0TATAPCGdTANoUnf0Unf0953953GasAExYSBrkr95369401647002131TA7TF1Attchd1999RFn2644.100240TATAY-80.348891113.043436125.521880000082007WDNormal175000
145620RLD13175PavedRegLvlAllPubIGSNWAmesNormNorm1Fam1Story6619781988GableSSPlywoodPlywood119.0TATACBGdTANoALQ790Rec1635891542GasATAYSBrkr2073002073102031TA7MD12Attchd1978Unf2180.864203TATAY36.180338221.514480148.266666000022010WDNormal210000
145770RLD9042PavedRegLvlAllPubIGSCrawforNormNorm1Fam2Story7919412006GableSSCemntBdCmentBd0.0ExGdSTAGdNoGLQ275Unf08771152GasAExYSBrkr1188115202340002041Gd9TF2Attchd1941RFn1439.363996TATAY88.568242110.88869054.320896000250052010WDNormal266500
145820RLD9717PavedRegLvlAllPubIGSNAmesNormNorm1Fam1Story5619501996HipSSMetalSdMetalSd0.0TATACBTATAMnGLQ49Rec102901078GasAGdYFuseA1078001078101021Gd5TF0Attchd1950Unf1603.143692TATAY144.036562-33.65485719.498763000042010WDNormal142125